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1
Hou, Juan, Haoran Li, and Natasa Nord. "Optimal control of secondary side supply water temperature for substation in district heating systems." E3S Web of Conferences 111 (2019): 06015. http://dx.doi.org/10.1051/e3sconf/201911106015.
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Low temperature is the most significant feature of the future district heating system- the 4th generation district heating (4GDH). However, a widely used control strategy for supply water temperature in substation is weather- compensated control. It is a feedforward control without any dynamic information about buildings, which can lead to higher or lower supply water temperature. This paper presents model predictive controller (MPC) applied to the supply water temperature control for substations in district heating systems. MPC is an advanced control technique, which can make full use of dynamic information of buildings to determine the optimal supply water temperature of substations. In this paper, a multiple inputs and single output dynamic model was identified by subspace methods. Two different MPC controllers were designed in Simulink. The MPC controller 1 focused on keeping indoor air temperature at reference values. The MPC controller 2 focused on both keeping indoor air temperature at reference values and tracking the minimum supply water temperature in order to find the temperature potential for the future DH systems. Both of the MPC controllers proved to have a better tracking effect for indoor air temperature and lower average supply temperatures compared to weather- compensated. The MPC controller 2 could further lower supply water temperature compared to the MPC controller 1 by tracking minimum supply water temperature in its objective function. The average supply water temperatures for the weather- compensated, the MPC controller1, and the MPC controller 2 were 52°C, 51°C and 50°C, respectively. The results showed that MPC has a great potential in the area of supply water temperature control of the district heating systems.
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Wahid, Abdul, and Naufal Syafiq Maro. "Multi Input Multi Output (MIMO) Control 2x2 at Vacuum Distillation Unit for LVGO, MVGO, and HVGO Production." E3S Web of Conferences 67 (2018): 03012. http://dx.doi.org/10.1051/e3sconf/20186703012.
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Currently, Indonesia is still experiencing a fuel deficit, so it is necessary to build a new oil refinery and a process optimization at an existing refinery. A vacuum distillation unit (VDU) is used to process the atmospheric residue products from crude distillation unit (CDU). A multivariable model predictive control (MMPC) is proposed to improve a control performance in VDU because of the interaction between variables in the unit. Therefore, it is necessary to find the variables that interact with each other. In this study only two variables are discussed. Set point (SP) and disturbance changes are used to test the control performance with integral of square error (ISE) as the indicator. The results are compared with the control performance of the PI controller and a single MPC. As a result, the feed flow rate and bottom-stage temperature are strongest interactions so that both are determined as controlled variables in MMPC. The control performance of MMPC is better than the PI controller and the single MPC with control performance improvement of 48% to the PI controller and 21% to MPC on for Feed Flow Rates, and 98% to the PI controller and 27% to MPC on Bottom Stage Temperature. While on disturbance changes the enhancement is 35% for the Bottom Stage Temperature.
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Wahid, Abdul, and Richi Adi. "MODELING AND CONTROL OF MULTIVARIABLE DISTILLATION COLUMN USING MODEL PREDICTIVE CONTROL USING UNISIM." SINERGI 20, no. 1 (February 1, 2016): 14. http://dx.doi.org/10.22441/sinergi.2016.1.003.
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Distillation columns are widely used in chemical industry as unit operation and required advance process control because it has multi input multi output (MIMO) or multi-variable system, which is hard to be controlled. Model predictive control (MPC) is one of alternative controller developed for MIMO system due to loops interaction to be controlled. This study aimed to obtain dynamic model of process control on a distillation column using MPC, and to get the optimum performance of MPC controller. Process control in distillation columns performed by simulating the dynamic models of distillation columns by UNISIM R390.1 software. The optimization process was carried out by tuning the MPC controller parameters such as sampling time (Ts = 1 – 240 s), prediction horizon (P = 1-400), and the control horizon (M=1-400). The comparison between the performance of MPC and PI controller is presented and Integral Absolut Error (IAE) was used as comparison parameter. The results indicate that the performance of MPC was better than PI controller for set point change 0.95 to 0.94 on distillate product composition using a modified model 1 with IAE 0.0584 for MPC controller and 0.0782 for PI controller.
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Kumavat, Mayur, and Sushil Thale. "Analysis of CSTR Temperature Control with PID, MPC & Hybrid MPC-PID Controller." ITM Web of Conferences 44 (2022): 01001. http://dx.doi.org/10.1051/itmconf/20224401001.
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This paper presents an analysis of the continuous stirred tank reactor (CSTR) temperature control with the Proportional-Integral-Derivative (PID) Controller, Model Predictive Controller (MPC) and Hybrid-Model Predictive Controller-Proportional Integral Derivative Controller (MPC-PID). It is the main goal of this project to find a suitable improvement strategy for the system’s stability and accuracy to be more stable. By creating a model, the control system is implemented for all the above mentioned control methods and so comparative analysis is carried out to find the best control method for CSTR. Simulation data inspector is used to compare the performance of different types of control systems: PID, MPC and MPC-PID. It has been observed that the hybrid MPC-PID has a more effective control action than a PID controller; with some tuning, the MPC controller can maintain the temperature within a reference or set point range. The control and simulation toolbox is used to construct the model predictive control method in LabVIEW platform. The performance of controllers is measured in terms of settling time, rise time and percentage of overshoot. Finally, a comparative overview of PID, MPC and Hybrid MPC-PID controllers on system performance is presented and discussed.
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Kümpel, Alexander, Phillip Stoffel, and Dirk Müller. "Self-adjusting model predictive control for modular subsystems in HVAC systems." Journal of Physics: Conference Series 2042, no. 1 (November 1, 2021): 012037. http://dx.doi.org/10.1088/1742-6596/2042/1/012037.
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Abstract In order to reduce the energy consumption and CO2 emissions in the building sector, an efficient control strategy, such as model predictive control (MPC) is required. However, MPC is rarely applied in buildings since the implementation and modeling is complex, time consuming and costly. To bring MPC into practice, controllers and models are needed, that automatically adapt their behavior to the controlled system. In this work, such a self-adjusting MPC applicable to heating, ventilation and air-conditioning (HVAC) systems is developed. The MPC is based on a simple grey-box model that is able to cover the general dynamics of the considered subsystem. The controller adapts the model parameters online according to the past measurements of the controlled system using a moving horizon estimation. The developed self-adjusting MPC is applied to three heating coils in a simulation. Compared with a PID controller, the self-adjusting MPC is able to increase the control quality up to 10%, while no manual tuning is needed. Additionally, the model predictive approach is able to reduce the power consumption of the pump by 80%.
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Rezaee, Alireza. "Model predictive Controller for Mobile Robot." Transactions on Environment and Electrical Engineering 2, no. 2 (June 27, 2017): 18. http://dx.doi.org/10.22149/teee.v2i2.96.
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This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is implemented on a real robot. The comparison between a PID controller, adaptive controller, and the MPC illustrates advantage of the designed controller and its ability for exact control of the robot on a specified guide path.
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Xu, Ying, Wentao Tang, Biyun Chen, Li Qiu, and Rong Yang. "A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles." Symmetry 13, no. 3 (February 26, 2021): 381. http://dx.doi.org/10.3390/sym13030381.
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Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.
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Vrečko, D., N. Hvala, and M. Stražar. "The application of model predictive control of ammonia nitrogen in an activated sludge process." Water Science and Technology 64, no. 5 (September 1, 2011): 1115–21. http://dx.doi.org/10.2166/wst.2011.477.
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In this paper a model predictive controller (MPC) for ammonia nitrogen is presented and evaluated in a real activated sludge process. A reduced nonlinear mathematical model based on mass balances is used to model the ammonia nitrogen in the activated sludge plant. An MPC algorithm that minimises only the control error at the end of the prediction interval is applied. The results of the ammonia MPC were compared with the results of the ammonia feedforward-PI and ammonia PI controllers from our previous study. The ammonia MPC and ammonia feedforward-PI controller give better results in terms of ammonia removal and aeration energy consumption than the ammonia PI controller because of the measurable disturbances used. On the other hand, with the ammonia MPC, comparable or even slightly poorer results than with the ammonia feedforward-PI controller are obtained. Further improvements to the MPC could be possible with an improved accuracy of the nonlinear reduced model of the ammonia nitrogen, more sophisticated control criteria used inside the controller and the extension of the problem from univariable ammonia to multivariable total nitrogen control.
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Yasmine Begum, A., and G. V. Marutheeswar. "Design of MPC for Superheated Steam Temperature Control in a Coal-fired Thermal Power Plant." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 1 (October 1, 2016): 73. http://dx.doi.org/10.11591/ijeecs.v4.i1.pp73-82.
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<p>A superheater is a vital part of the steam generation process in the boiler-turbine system. Reliable control of temperature in the superheated steam temperature system is essential to guarantee efficiency and high load-following capability in the operation of coal-fired Thermal power plant. The PI and PID controllers are extensively used in cascade control of secondary superheated steam temperature process.The design and implementation of a Model Predictive Control (MPC) strategy for the superheated steam temperature regulation in a thermal power plant is presented. A FOPTD model is derived from the dynamic model of the superheater. This model is required by the MPC algorithm to calculate the future control inputs. A new MPC controller is designed and its performance is tested through simulation studies. Compared with the superheater steam temperature control using a conventional PID controller, the steam temperature controlled by the MPC controller is found to be more stable. The stable steam temperature leads to energy saving and efficient plant operation, as verified by the simulation results. </p>
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Hu, Dawei, Gangyan Li, and Feng Deng. "Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System." Processes 9, no. 5 (May 20, 2021): 899. http://dx.doi.org/10.3390/pr9050899.
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This paper presents a control-oriented Linear Parameter-Varying (LPV) model for commercial vehicle air brake systems with the electro-pneumatic proportional valve based on the nonlinear mathematical model, a set of discrete-time linearized models at different target pressures with the q-Markov Cover system identification method. The scheduled parameters for the LPV model were the brake chamber pressure, which was controlled by the electro-pneumatic proportional valve. On the basis of the LPV model, a family of Model Predictive Control (MPC) controllers with a Kalman filter was designed at each operation point. Then, the gain-scheduled MPC was designed over the entire operating range with the switched strategy, which was validated by experimental data. Furthermore, compared with the PID controller, the performance of the system was improved with a gain-scheduled MPC controller.
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Taherian, Shayan, Kaushik Halder, Shilp Dixit, and Saber Fallah. "Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation." Sensors 21, no. 13 (June 23, 2021): 4296. http://dx.doi.org/10.3390/s21134296.
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Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints.
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Sokolov, Vladimir, Oleg Krol, Vladislav Andriichuk, Irina Chernikova, and Tatiana Shevtsova. "Improvement of HVAC systems based on adaptive predictive control." E3S Web of Conferences 420 (2023): 07020. http://dx.doi.org/10.1051/e3sconf/202342007020.
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The paper considers the issue of approbation of adaptive predictive control for heating, ventilation and air conditioning systems, shows the possibility of improving the regulation processes by its application on example of ventilation system. The idea of control using predictive model is presented, the principles of control using MPC controller are noted, the controller structure and the criterion for choosing the optimal values of control signal are considered. The feature of adaptive predictive control is the presence of the mathematical model for control object, which accurately describes its behavior. The MPC controller determines the sequence of control signal values that provides the best predicted trajectory for controlled variable. The implementation of the MPC approach is shown on the example of supply VAV ventilation system of the classroom. In the considered ventilation system, the change of heat load for the room is compensated by the change of amount of supply air coming from the central supply ventilation unit at its constant temperature. To simulate ventilation system in the Simulink environment of the MATLAB application package, the block diagram was developed, and the Model Predictive Control Toolbox was used to synthesize the MPC controller. The study of transient processes in VAV ventilation system was carried out, transient process in the system without controller, with PID controller and MPC controller were compared. Comparison of the results showed that the use of the MPC controller makes it possible to improve the regulation process of thermal regime in the room by increasing the regulation quality.
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Liu, Fang, Feng Gao, Ling Liu, and Denis N. Sidorov. "IPMSM Speed and Current Controller Design for Electric Vehicles Based on Explicit MPC." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 6 (November 20, 2019): 1019–26. http://dx.doi.org/10.20965/jaciii.2019.p1019.
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The difficulties in implementing the model predictive control (MPC) in interior permanent-magnet synchronous motors (IPMSMs) consist of the nonlinear behavior of IPMSMs and the computational effort required by MPC. This paper presents an IPMSM controller design method for electric vehicles based on explicit MPC (EMPC), which uses a different linearization method. The proposed controller combines the speed and current controllers and replaces the traditional cascade structure. First, the nonlinear terms in the system model are added into the control input as voltage compensation to obtain a simple linear model. Next, the proposed controller based on MPC is designed, which considers the effects of load torque and uses an increment model. Furthermore, the controller applies both current and voltage constraints. The EMPC method based on a binary search is used to accelerate the solution of the optimization problem. Finally, the simulation results show the validity and superiority of the proposed method.
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Alamirew, Tesfaye, V. Balaji, and Nigus Gabbeye. "Comparison of PID Controller with Model Predictive Controller for Milk Pasteurization Process." Bulletin of Electrical Engineering and Informatics 6, no. 1 (March 1, 2017): 24–35. http://dx.doi.org/10.11591/eei.v6i1.575.
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Proportional–Integral–Derivative (PID) controllers are used in many of the Industries for various process control applications. PID controller yields a long settling time and overshoot which is not good for the process control applications. PID is not suitable for many of the complex process control applications. This research paper is about developing a better type of controller, known as MPC (Model Predictive Control). The aim of the paper is to design MPC and PID for a pasteurization process. In this manuscript comparison of PID controller with MPC is made and the responses are presented. MPC is an advanced control strategy that uses the internal dynamic model of the process and a history of past control moves and a combination of many different technologies to predict the future plant output. The dynamics of the pasteurization process was estimated by using system identification from the experimental data. The quality of different model structures was checked using best fit with data validation, residual and stability analysis. Auto-regressive with exogenous input (ARX322) model was chosen as a model structure of the pasteurization process and fits about 80.37% with datavalidation. MPC and PID control strategies were designed using ARX322 model structure. The controller performance was compared based on settling time, percent of overshoot and stability analysis and the results are presented.
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Kennedy, Okokpujie, Emmanuel Chukwu, Olamilekan Shobayo, Etinosa Noma-Osaghae, Imhade Okokpujie, and Modupe Odusami. "Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 359. http://dx.doi.org/10.11591/ijece.v9i1.pp359-368.
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This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated.
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Pasha, SK Abdul, and N. Prema Kumar. "Model Predictive Controller based Unified Power Quality Conditioner for Voltage Regulation Studies in 33- Bus Closed Loop Distribution System." E3S Web of Conferences 184 (2020): 01073. http://dx.doi.org/10.1051/e3sconf/202018401073.
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Recent developments in FACTS have produced U.P.Q.C to mitigate sag and attenuate THD. U.P.Q.C has been urbanized as a FACTS controller between feeding end & far end of distribution system .The U-P-Q-C is capable of improving the voltage profile & reducing THD of distribution system by regulating the voltage using PR (Proportional-Resonant-Controller) and MPC (Model-Predictive) controller. This work proposes U-P-Q-C for Thirty Three Bus Systems .The objective of this work is to enhance-voltage-profile of T-T-B-S. The T-T-B-S in open loop & closed loop-TTBS- U-P-Q-C using PR and MPC-controllers are-modeled,pretend & their consequences are represented. Responses are estimated as a time of settle and error in steady state. The outcomes indicate that MP Controlled T-T-B-S system has better response than PR controlled T-T-B-S system.
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Alasali, Feras, Stephen Haben, Husam Foudeh, and William Holderbaum. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads." Energies 13, no. 10 (May 20, 2020): 2596. http://dx.doi.org/10.3390/en13102596.
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This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems.
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Lio, Wai Hou, John Anthony Rossiter, and Bryn Llywelyn Jones. "Modular Model Predictive Control upon an Existing Controller." Processes 8, no. 7 (July 16, 2020): 855. http://dx.doi.org/10.3390/pr8070855.
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The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.
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Munoz, Samuel Arce, Junho Park, Cristina M. Stewart, Adam M. Martin, and John D. Hedengren. "Deep Transfer Learning for Approximate Model Predictive Control." Processes 11, no. 1 (January 7, 2023): 197. http://dx.doi.org/10.3390/pr11010197.
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Transfer learning is a machine learning technique that takes a pre-trained model that has already been trained on a related task, and adapts it for use on a new, related task. This is particularly useful in the context of model predictive control (MPC), where deep transfer learning is used to improve the training of the MPC by leveraging the knowledge gained from related controllers. One way in which transfer learning is applied in the context of MPC is by using a pre-trained deep learning model of the MPC, and then fine-tuning the controller training for a new process automation task. This is similar to how an equipment operator quickly learns to manually control a new processing unit because of related skills learned from controlling the prior unit. This reduces the amount of data required to train the approximate MPC controller, and also improves the performance on the target system. Additionally, learning the MPC actions alleviates the computational burden of online optimization calculations, although this approach is limited to learning from systems where an MPC has already been developed. The paper reviews approximate MPC formulations with a case study that illustrates the use of neural networks and transfer learning to create a multiple-input multiple-output (MIMO) approximate MPC. The performance of the resulting controller is similar to that of a controller trained on an existing MPC, but it requires less than a quarter of the target system data for training. The main contributions of this paper are a summary survey of approximate MPC formulations and a motivating case study that includes a discussion of future development work in this area. The case study presents an example of using neural networks and transfer learning to create a MIMO approximate MPC and discusses the potential for further research and development in this area. Overall, the goal of this paper is to provide an overview of the current state of research in approximate MPC, as well as to inspire and guide future work in transfer learning.
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Wang, Xinyu, Xiao Ye, Yipeng Zhou, and Cong Li. "Path-Following Control of Unmanned Vehicles Based on Optimal Preview Time Model Predictive Control." World Electric Vehicle Journal 15, no. 6 (May 21, 2024): 221. http://dx.doi.org/10.3390/wevj15060221.
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In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC). The strategy includes the longitudinal speed limit, the optimal preview time surface, and the model predictive control (MPC)controller. The longitudinal speed limit controls speed to prevent vehicle rollover and sideslip. The optimal preview time surface adjusts the preview time according to the vehicle speed and path curvature. The preview point determined by the preview time is used as the reference waypoint of OP-MPC controller. Finally, the effectiveness of the strategy was verified through simulation and with the real unmanned vehicle. The maximum lateral deviation obtained by the OP-MPC controller was reduced from 0.522 m to 0.145 m under the simulation compared with an MPC controller. The maximum lateral deviation obtained by the OP-MPC controller was reduced from 0.5185 m to 0.2298 m under the real unmanned vehicle compared with the MPC controller.
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Han, Wenyao, Aijuan Li, Xin Huang, Wei Li, Jiaping Cao, and Haixiang Bu. "Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control." Advances in Mechanical Engineering 14, no. 4 (April 2022): 168781322210899. http://dx.doi.org/10.1177/16878132221089909.
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In order to improve the accuracy of trajectory tracking of in-wheel motor electric vehicles, a preview time adaptive trajectory tracking method based on iterative algorithm and fuzzy control is proposed. Firstly, based on the vehicle’s three-degree-of-freedom model, the vehicle is controlled to track trajectory based on model predictive control (MPC). The preview step size and sampling period of MPC are adjusted by iterative function and fuzzy controller, respectively. Then, In order to optimize MPC active steering control, a differential torque controller is established to realize the trajectory tracking control of differential torque steering. Finally, Carsim/Simulink co-simulation analysis and real vehicle verification are done. The simulation results show that the controller can complete the trajectory tracking control of the in-wheel motor intelligent vehicle, and the stability and steering performance are good. The controller has good robustness and adaptability according to road adhesion conditions and vehicle speed changes. At the same time, the trajectory tracking accuracy of the MPC controller is better than sliding mode variable structure control (SMC). The real vehicle verification results show that when the real vehicle tracking under different speeds, the adaptive preview time controller designed in this paper has good trajectory tracking performance and stability.
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Henmi, Tomohiro. "Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing." Journal of Robotics and Mechatronics 28, no. 5 (October 20, 2016): 695–701. http://dx.doi.org/10.20965/jrm.2016.p0695.
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[abstFig src='/00280005/11.jpg' width='300' text='ANMPC controller' ] The parameter-tuning method we discuss is for an Adaptive Nonlinear Model Predictive Controller (ANMPC). The MPC is optimization-based controller and decides control input to realize system output that tracks a reference trajectory through “optimal computation.” The reference trajectory is ideal trajectory of system output to converge on a desired value, i.e. controlled system performance depends on the reference trajectory. As a MPC controller which applies to the nonlinear systems, our group has already proposed an adaptive nonlinear MPC (ANMPC) for a tracking control problem of nonlinear two-link planar manipulators. This ANMPC uses a new reference trajectory having control parameters that must be tuned based on the desired controlled system’s responses and properties. To reduce troublesome parameter tuning, we propose new parameter-tuning method for ANMPC by a quantitative analysis of the relationship between a system’s behavior and ANMPC parameters. Numerically simulating the two-link nonlinear manipulator’s tracking control under various conditions demonstrates that proposed tuning method tunes the ANMPC effectively.
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Rouzbeh, Behrad, and Gary M. Bone. "Optimal Force Allocation and Position Control of Hybrid Pneumatic–Electric Linear Actuators." Actuators 9, no. 3 (September 14, 2020): 86. http://dx.doi.org/10.3390/act9030086.
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Hybrid pneumatic–electric actuators (HPEAs) are redundant actuators that combine the large force, low bandwidth characteristics of pneumatic actuators with the large bandwidth, small force characteristics of electric actuators. It has been shown that HPEAs can provide both accurate position control and high inherent safety, due to their low mechanical impedance, making them a suitable choice for driving the joints of assistive, collaborative, and service robots. If these characteristics are mathematically modeled, input allocation techniques can improve the HPEA’s performance by distributing the required input (force or torque) between the redundant actuators in accordance with each actuator’s advantages and limitations. In this paper, after developing a model for a HPEA-driven system, three novel model-predictive control (MPC) approaches are designed that solve the position tracking and input allocation problem using convex optimization. MPC is utilized since the input allocation can be embedded within the motion controller design as a single optimization problem. A fourth approach based on conventional linear controllers is included as a comparison benchmark. The first MPC approach uses a model that includes the dynamics of the payload and pneumatics; and performs the motion control using a single loop. The latter methods simplify the MPC law by separating the position and pressure controllers. Although the linear controller was the most computationally efficient, it was inferior to the MPC-based controllers in position tracking and force allocation performance. The third MPC-based controller design demonstrated the best position tracking with RMSE of 46%, 20%, and 55% smaller than the other three approaches. It also demonstrated sufficient speed for real-time operation.
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Gulzar, Muhammad, Syed Rizvi, Muhammad Javed, Daud Sibtain, and Rubab Salah ud Din. "Mitigating the Load Frequency Fluctuations of Interconnected Power Systems Using Model Predictive Controller." Electronics 8, no. 2 (February 1, 2019): 156. http://dx.doi.org/10.3390/electronics8020156.
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The penetration of renewable energy sources into the conventional power systems are evolving day by day. Therefore, in this paper, a photovoltaic (PV) connected thermal system is discussed and analyzed by keeping PV to operate at maximum power point (MPP). The main problem in the interconnection of these systems is load frequency fluctuations due to different load changing conditions. The model predictive controller (MPC) has the ability to predict the target value at real-time with fast convergence. Therefore, MPC is proposed to negate this problem by giving minimum oscillation. The comparison analysis is carried out with other conventional controllers, including genetic algorithm-based PI, firefly algorithm-based PI and PI controller. Simulation results clearly exhibit the outclass performance of MPC over all other controllers.
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Kolodin, Aleksey A., and Viktor V. Elshin. "Research and development of the controller based on the model predictive control." Vestnik of Samara State Technical University. Technical Sciences Series 29, no. 1 (April 23, 2021): 36–45. http://dx.doi.org/10.14498//tech.2021.1.3.
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Modern automated process control systems that use programmable logic controllers use software controllers based on the proportional integral-differential control law, the PID controller. In most cases, this regulator is sufficient for conducting the technological process. It has high performance with configurable and sufficient quality of regulation. However, using a PID controller for non-linear, poorly defined, multi-connected objects with a long delay time can lead to unstable control quality indicators, accumulation of errors, and ultimately to a deterioration in product quality. One of the most promising methods of control is Model Predictive Control - MPC. The method base on predictive models of control objects. The quality of the controller's control depends on how well the system dynamics described by the model used to design the controller. In most cases, MPC-based control use to handle optimal control problems on the Manufacturing Execution System-MES. However, thanks to the development of microprocessors and increased CPU performance, it becomes possible to apply the principles of control with a predictive model at a lower level, and perform real-time operational control in optimal modes. The work presents the algorithm of MPC controller. The control object is a SISO object with a nonlinear characteristic and a long transition process. Studies of the developed MPC regulator showed that the quality of regulation, compared to the PID regulator, increased by more than 20%, the time to get to set point decreased, and there was almost no over-regulation. The most effective application of the MPC controller is seen in processes with long transitions and with a significant delay time.
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Berouine, Anass, Radouane Ouladsine, Mohamed Bakhouya, and Mohamed Essaaidi. "Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings." Energies 13, no. 12 (June 23, 2020): 3246. http://dx.doi.org/10.3390/en13123246.
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Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement.
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Essa, Mohamed El-Sayed M., Mahmoud Elsisi, Mohamed Saleh Elsayed, Mohamed Fawzy Ahmed, and Ahmed M. Elshafeey. "An Improvement of Model Predictive for Aircraft Longitudinal Flight Control Based on Intelligent Technique." Mathematics 10, no. 19 (September 26, 2022): 3510. http://dx.doi.org/10.3390/math10193510.
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This paper introduces a new intelligent tuning for the model predictive control (MPC) based on an effective intelligent algorithm named the bat-inspired algorithm (BIA) for the aircraft longitudinal flight. The tuning of MPC horizon parameters represents the main challenge to adjust the system performance. So, the BIA algorithm is intended to overcome the tuning issue of MPC parameters due to conventional methods, such as trial and error or designer experience. The BIA is adopted to explore the best parameters of MPC based on the minimization of various time domain objective functions. The suggested aircraft model takes into account the aircraft dynamics and constraints. The nonlinear dynamics of aircraft, gust disturbance, parameters uncertainty and environment variations are considered the main issues against the control of aircraft to provide a good flight performance. The nonlinear autoregressive moving average (NARMA-L2) controller and proportional integral (PI) controller are suggested for aircraft control in order to evaluate the effectiveness of the proposed MPC based on BIA. The proposed MPC based on BIA and suggested controllers are evaluated against various criteria and functions to prove the effectiveness of MPC based on BIA. The results confirm that the accomplishment of the suggested BIA-based MPC is outstanding to the NARMA-L2 and traditional PI controllers according to the cross-correlation criteria, integral time absolute error (ITAE), system overshoot, response settling time, and system robustness.
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Gounder, Yasoda Kailasa, and Sowkarthika Subramanian. "Application of machine learning controller in matrix converter based on model predictive control algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (September 1, 2023): 1489. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1489-1496.
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Finite control set model predictive control (FCS-MPC) algorithms are famous in power converter for its easy implementation of constraints with cost function than classical control algortihms. However computation complexity increases when swicthing state is high for converters such as matrix converter, multilevel converters and this impose a serious drawback to compute multi-step prediction horizon MPC algorithm which further increases the computation. To overcome the above said difficulty, machine learning based artificial neural network (ANN) controller for matrix converter is proposed. The training data for ANN controller is derived from MPC algorithm and trained offline with an accuracy of 70.3%. The proposed ANN controller shows a similar and better performance than MPC controller in terms of total harmonic distortion (THD), peak overshoot during dynamic change in reference current and dynamic change in load parameter and less computation with less execution time. Further, ANN controller for matrix converter is tested in OPAL-RT using hardware in-loop (HIL) simulation and showed that it outperforms MPC controller.
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Albalawi, Hani, and Sherif A. Zaid. "Performance Improvement of a Grid-Tied Neutral-Point-Clamped 3-φ Transformerless Inverter Using Model Predictive Control." Processes 7, no. 11 (November 15, 2019): 856. http://dx.doi.org/10.3390/pr7110856.
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Grid-connected photovoltaic (PV) systems are now a common part of the modern power network. A recent development in the topology of these systems is the use of transformerless inverters. Although they are compact, cheap, and efficient, transformerless inverters suffer from chronic leakage current. Various researches have been directed toward evolving their performance and diminishing leakage current. This paper introduces the application of a model predictive control (MPC) algorithm to govern and improve the performance of a grid-tied neutral-point-clamped (NPC) 3-φ transformerless inverter powered by a PV panel. The transformerless inverter was linked to the grid via an inductor/capacitor (LC) filter. The filter elements, as well as the internal impedance of the grid, were considered in the system model. The discrete model of the proposed system was determined, and the algorithm of the MPC controller was established. Matlab’s simulations for the proposed system, controlled by the MPC and the ordinary proportional–integral (PI) current controller with sinusoidal pulse width modulation (SPWM), were carried out. The simulation results showed that the MPC controller had the best performance for earth leakage current, total harmonic distortion (THD), and the grid current spectrum. Also, the efficiency of the system using the MPC was improved compared to that using a PI current controller with SPW modulation.
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Negri, Gabriel Hermann, Arthur Garcia Bartsch, Mariana Santos Matos Cavalca, and Ademir Nied. "Frequency Response Comparison of PI-Based FOC and Cascade-Free MPC using 1 kHz SVM Applied to PMSM drive." Journal of Applied Instrumentation and Control 5, no. 2 (April 11, 2018): 1. http://dx.doi.org/10.3895/jaic.v5n2.5945.
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Abstract— This paper presents a 1 kHz SVM-FOC (Space Vector Modulation with Field Oriented Control) drive system for a Permanent Magnet Synchronous Motor, using different control strategies. Such strategies are internal model and frequency response designed PI (Proportional and Integral) controllers and a multivariable MPC (Model Predictive Control) controller using a state-space prediction model. This MPC method becomes interesting for improving the closed-loop speed frequency response, since it results in a cascade-free controller. The performance of each controller was evaluated in a qualitative manner through simulations and quantitatively by load torque and speed reference AC sweeps, generating dynamic stiffness curves and Bode diagrams for the utilized techniques. Results show that the MPC approach is useful for enabling fast dynamic responses with the reduced switching frequency, which reduces the drive system cost and improves its efficiency.
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Aboelhassan, Ahmed, M. Abdelgeliel, Ezz Eldin Zakzouk, and Michael Galea. "Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications." Energies 13, no. 24 (December 14, 2020): 6594. http://dx.doi.org/10.3390/en13246594.
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Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative (PID) controller is a common industrial controller known for its simplicity and robustness. Adapting the parameters of the PID considering system constraints is a challenging task. Both controllers, MPC and PID, merged in a hierarchical structure in this work to improve the industrial processes performance considering the operational constraints. The proposed control system is simulated and implemented on a three-tank benchmark system as a Multi-Input Multi-Output (MIMO) system. Since the main industrial goal of the proposed configuration is to be easily implemented using the available automation technology, PID controller is implemented in a PLC (Programable Logic Controller) controller as a lower controller level, while MPC controller and the adaptation mechanism are implemented within a SCADA (Supervisory Control And Data Acquisition) system as a higher controller level.
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Zahir Zulkifly, Siti Hajar Yusoff, Nor Liza Tumeran, and Nur Syazana Izzati Razali. "Battery Energy Storage System (BESS) Modeling for Microgrid." IIUM Engineering Journal 24, no. 1 (January 4, 2023): 57–74. http://dx.doi.org/10.31436/iiumej.v24i1.2435.
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In the age of technology, microgrids have become well known because of their capability to back up the grid when an unpleasant event is about to occur or during power disruptions, at any time. However, the microgrid will not function well during power disruptions if the controller does not respond fast enough and the BESS will be affected. Many types of controllers can be used for microgrid systems. The controllers may take the form of Maximum Power Point Tracking (MPPT) Controller, Proportional Integral Derivative (PID) Controller, and Model Predictive Controller (MPC). Each of the controllers stated has its functions for the microgrid. However, two controllers that must be considered are PID and MPC. Both controllers will be compared based on their efficiency results which can be obtained through simulations by observing both graphs in charging and discharging states. Most researchers implied that MPC is better than PID because of several factors such as MPC is more robust and stable because of its complexity. Other than that, MPC can handle more inputs and outputs than PID which can cater to one input and output only. Although MPC has many benefits over the PID, still it is not ideal due to its complex algorithm. This work proposed an algorithm of simulations for the MPC to operate to get the best output for microgrid and BESS and compare the performance of MPC with PID. Using Simulink and MATLAB as the main simulation software is a very ideal way to simulate the dynamic performance of MPC. Furthermore, with Simulink, unpredictable variables such as Renewable Energy (RE) sources input and loads demands that are related to MPC can be measured easily. The algorithm of MPC is a cost function. Then the performance of the MPC is calculated using Fast-Fourier Transform (FFT) and Total Harmonic Distortion (THD). Lower THD means a higher power factor, this results in higher efficiency. This paper recorded THD of 9.57% and 12.77% in charging states and 16.51% and 18.15% in discharging states of MPC. Besides, PID recorded THD of 22.10% and 29.73% in charging states and 84.29% and 85.58% in discharging states. All of the recorded THD is below 25% in MPC and it shows a good efficiency while PID’s THD is above 25% shows its inefficiency. ABSTRAK: Pada zaman teknologi, mikrogrid menjadi terkenal kerana keupayaannya untuk menjana kuasa grid apabila kejadian yang tidak menyenangkan bakal berlaku atau ketika terjadinya gangguan kuasa, pada bila-bila masa. Walau bagaimanapun, mikrogrid tidak dapat berfungsi dengan baik semasa gangguan kuasa jika alat kawalan tidak bertindak balas dengan cukup pantas dan BESS akan terjejas. Banyak alat kawalan (pengawal) boleh digunakan bagi keseluruhan sistem mikrogrid. Setiap pengawal adalah berbeza seperti Pengawal Penjejakan Titik Kuasa Maksimum (MPPT), Pengawal Berkadar Terbitan Kamilan (PID) dan Pengawal Model Ramalan (MPC). Setiap pengawal yang dinyatakan mempunyai fungsinya yang tersendiri bagi mikrogrid. Walau bagaimanapun, dua pengawal yang perlu dipertimbangkan adalah PID dan MPC. Kedua-dua pengawal ini akan dibandingkan berdasarkan keputusan kecekapan yang boleh didapati melalui simulasi dengan memerhati kedua-dua graf pada keadaan pengecasan dan nyahcas. Ramai penyelidik menganggap bahawa MPC adalah lebih baik berbanding PID kerana beberapa faktor seperti MPC lebih teguh dan stabil kerana kerumitannya. Selain itu, MPC dapat mengendalikan lebih banyak input dan output berbanding PID yang hanya dapat menyediakan satu input dan output sahaja. Walaupun MPC mempunyai banyak faedah berbanding PID, ianya masih tidak sesuai kerana algoritma yang kompleks. Kajian ini mencadangkan algoritma simulasi bagi MPC beroperasi mendapatkan output terbaik untuk mikrogrid dan BESS dan membandingkan prestasi MPC dengan PID. Perisian simulasi utama yang sangat ideal bagi mensimulasi prestasi dinamik MPC adalah dengan menggunakan Simulink dan MATLAB. Tambahan, dengan Simulink, pembolehubah yang tidak terjangka seperti sumber Tenaga Boleh Diperbaharui (RE) dan permintaan beban yang berkaitan MPC boleh diukur dengan mudah. Algoritma MPC adalah satu fungsi kos. Kemudian prestasi MPC dikira menggunakan Penjelmaan Fourier Pantas (FFT) dan Total Pengherotan Harmonik (THD). THD yang lebih rendah bermakna faktor kuasa meningkat, ini menghasilkan kecekapan yang lebih tinggi. Kajian ini mencatatkan THD sebanyak 9.57% dan 12.77% dalam keadaan mengecas dan 16.51% dan 18.15% dalam keadaan nyahcas oleh MPC. Selain itu, PID mencatatkan THD sebanyak 22.10% dan 29.73% dalam keadaan mengecas dan 84.29% dan 85.58% dalam keadaan nyahcas. Semua THD yang direkodkan adalah di bawah 25% bagi MPC dan ia menunjukkan kecekapan yang baik manakala THD bagi PID adalah melebihi 25% menunjukkan ketidakcekapan.
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Shaout, Adnan, Syed Ahmad, and Dan Osborn. "Comparison of Fuzzy Logic Control and Model Predictive Control for a Smart Adaptive Cruise Control Vehicle System." Jordan Journal of Electrical Engineering 10, no. 1 (2024): 27. http://dx.doi.org/10.5455/jjee.204-1687812578.
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Adaptive Cruise Control (ACC), Cruise Control (CC), and Automatic Emergency Braking (AEB) serve as the basis of longitudinal automated driving, and as Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller. The purpose of this paper is to design a fuzzy logic-based ACC, CC, and AEB controller and compare it to an equivalent MPC controller. All three controllers control the desired longitudinal acceleration, and their functionality is tested using Matlab’s Fuzzy Logic Designer and other Simulink toolboxes. Ultimately, the results of the analysis demonstrate that the proposed fuzzy controller operates just as well if not better than the MPC controller and that the fuzzy controller is able to operate well in all tested scenarios.
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Bak, Yeongsu. "Dynamic Characteristic Improvement of Integrated On-Board Charger Using a Model Predictive Control." Energies 15, no. 22 (November 21, 2022): 8745. http://dx.doi.org/10.3390/en15228745.
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This paper proposes a dynamic characteristic improvement of an integrated on-board charger (OBC) using a model predictive control (MPC) method. The integrated OBC performs both battery charging and starter generator (SG) driving for engine starting in plug-in hybrid electric vehicles (PHEVs). If it performs battery charging, battery-side voltage and battery-side current are control objects which are usually controlled by using a proportional-integral (PI) controller. However, it has the disadvantage of undesirable dynamic characteristics, and gain tuning of the PI controller is necessary to properly control the voltage and current. Therefore, this paper proposes the MPC method for the dynamic characteristic improvement of integrated OBC. It can achieve not only dynamic characteristic improvement, but also robustness from the abrupt change of load impedance. By using the proposed MPC method for integrated OBC, the settling time to control the output voltage is decreased by 50% in the transient state compared to that by using the PI controller. The effectiveness of the proposed MPC method is verified by simulation and experimental results.
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Tang, Meiling, and Shengxian Zhuang. "On Speed Control of a Permanent Magnet Synchronous Motor with Current Predictive Compensation." Energies 12, no. 1 (December 26, 2018): 65. http://dx.doi.org/10.3390/en12010065.
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In this study, a current model predictive controller (MPC) is designed for a permanent magnet synchronous motor (PMSM) where the speed of the motor can be regulated precisely. First, the mathematical model, the specifications, and the drive topology of the PMSM are introduced, followed by an elaboration of the design of the MPC. The MPC is then used to predict the current in a discrete-time calculation. The phase current at the next sampling step can be estimated to compensate the current errors, thereby modifying the three-phase currents of the motor. Next, Simulink modeling of the MPC algorithm is given, with three-phase current waveforms compared when the motor is operated under the designed MPC and a traditional vector control for PMSM. Finally, the speed responses are measured when the motor is controlled by traditional control methods and the MPC approach under varied speed references and loads. In comparison with traditional controllers, both the simulation and the experimental results suggest that the MPC for the PMSM can improve the speed-tracking performance of the motor and that this motor has a fast speed response and small steady-state errors under the rated load.
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Dickler, Sebastian, Thorben Wintermeyer-Kallen, János Zierath, Reik Bockhahn, Dirk Machost, Thomas Konrad, and Dirk Abel. "Full-scale field test of a model predictive control system for a 3 MW wind turbine." Forschung im Ingenieurwesen 85, no. 2 (April 9, 2021): 313–23. http://dx.doi.org/10.1007/s10010-021-00467-w.
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AbstractModel predictive control (MPC) is a strong candidate for modern wind turbine control. While the design of model predictive wind turbine controllers in simulations has been extensively investigated in academic studies, the application of these controllers to real wind turbines reveals open research challenges. In this work, we focus on the validation of a linear time-variant MPC system for a 3 MW wind turbine in a full-scale field test. First, the study proves the MPC’s capability to control the real wind turbine in the partial load region. Compared to the turbine’s baseline PID controller, the MPC system offers similar results for the electrical power output and for the occurring mechanical loads. Second, the study validates a previously proposed, simulation-based rapid control prototyping process for a systematic MPC development. The systematic development process allows to completely design and parameterize the MPC system in a simulative environment independent of the real wind turbine. Through the rapid control prototyping process, the MPC commissioning in the wind turbine’s programmable logic controller can be realized within a few hours without any modifications required in the field. Thus, this study establishes the proof of concept for a linear time-variant MPC system for a 3 MW wind turbine in a full-scale field test and bridges the gap between the control design and field testing of MPC systems for wind turbines in the multi-megawatt range.
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Hu, Zhen, Daqi Zhu, Caicha Cui, and Bing Sun. "Trajectory Tracking and Re-planning with Model Predictive Control of Autonomous Underwater Vehicles." Journal of Navigation 72, no. 2 (September 21, 2018): 321–41. http://dx.doi.org/10.1017/s0373463318000668.
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The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs.
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Corbett, John P., Jose Garcia-Tirado, Patricio Colmegna, Jenny L. Diaz Castaneda, and Marc D. Breton. "Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System." Journal of Diabetes Science and Technology 16, no. 1 (December 3, 2021): 52–60. http://dx.doi.org/10.1177/19322968211059159.
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Introduction: Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control. Methods: A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient’s total daily insulin (TDI) modulated by the disturbance’s likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module. Results: Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%). Conclusions: The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.
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Yehia Sayed Mohammed, Ahmed A. Zaki Diab, Ahmed G. Mahmoud A. Aziz, Hamdi Ali,. "Investigation of the Performance of Model Predictive Control for Induction Motor Drives." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 16, 2021): 1007–15. http://dx.doi.org/10.17762/itii.v9i1.235.
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The current work presents speed, torque and flux control of an induction motor (IM) drive, founded on model predictive control (MPC). Via the MPC techniques, the motor electromagnetic torque and flux linkage are controlled as an internal loop. However, the speed is controlled as the external loop. The internal control loop is founded on finite control set FCS-MPC, and the external control founded on the torque PI controller. The performance of the MPC is tested with various conditions of the drive operation, and the outcomes approve the excellent steady-state and dynamic operation of the system in a wide range of speeds and with torque disturbance.
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Ramos-Martinez, Moises, Carlos Alberto Torres-Cantero, Gerardo Ortiz-Torres, Felipe D. J. Sorcia-Vázquez, Himer Avila-George, Ricardo Eliú Lozoya-Ponce, Rodolfo A. Vargas-Méndez, Erasmo M. Renteria-Vargas, and Jesse Y. Rumbo-Morales. "Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network." Mathematics 11, no. 18 (September 19, 2023): 3967. http://dx.doi.org/10.3390/math11183967.
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This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model based on the HW model to maintain the bioethanol purity near 99% molar fraction. This work proposes an Artificial Neural Network (ANN) that captures the dynamics of the PSA model controlled by the MPC strategy. Both controllers are validated using the HW model of the PSA process, showing great performance and robustness against disturbances. The results show that we can follow the desired trajectory and attenuate disturbances, achieving the purity of bioethanol at a molar fraction value of 0.99 using the ANN based on the MPC strategy with 94% of fit in the control signal and a 97% fit in the purity signal, so we can conclude that our ANN can be used to attenuate disturbances and maintain purity in the PSA process.
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Tiwari, Madhur, Eric Coyle, and Richard J. Prazenica. "Direct-Adaptive Nonlinear MPC for Spacecraft Near Asteroids." Aerospace 9, no. 3 (March 15, 2022): 159. http://dx.doi.org/10.3390/aerospace9030159.
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In this work, we propose a novel controller based on a simple adaptive controller methodology and model predictive control (MPC) to generate and track trajectories of a spacecraft in the vicinity of asteroids. The control formulation is based on using adaptive control as a feedback controller and MPC as a feed-forward controller. The spacecraft system model, asteroid shape and inertia are assumed to be unknown, with the exception of the estimated total mass and angular velocity of the asteroid. The MPC is used to generate feed-forward trajectories and control input using only the mass and angular velocity of the asteroid combined with obstacle avoidance constraints. However, since the control input from MPC is calculated using only an approximated model of the asteroid, it fails to control the spacecraft in the presence of disturbances due to the asteroid’s irregular gravitational field. Hence, we propose an adaptive controller in conjunction with MPC to handle unknown disturbances. The numerical results presented in this work show that the novel control system is able to handle unknown disturbances while generating and tracking sub-optimal trajectories better than adaptive control or MPC solely.
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Okasha, Mohamed, Jordan Kralev, and Maidul Islam. "Design and Experimental Comparison of PID, LQR and MPC Stabilizing Controllers for Parrot Mambo Mini-Drone." Aerospace 9, no. 6 (June 1, 2022): 298. http://dx.doi.org/10.3390/aerospace9060298.
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Parrot Mambo mini-drone is a readily available commercial quadrotor platform to understand and analyze the behavior of a quadrotor both in indoor and outdoor applications. This study evaluates the performance of three alternative controllers on a Parrot Mambo mini-drone in an interior environment, including Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC). To investigate the controllers’ performance, initially, the MATLAB®/Simulink™ environment was considered as the simulation platform. The successful simulation results finally led to the implementation of the controllers in real-time in the Parrot Mambo mini-drone. Here, MPC surpasses PID and LQR in ensuring the system’s stability and robustness in simulation and real-time experiment results. Thus, this work makes a contribution by introducing the impact of MPC on this quadrotor platform, such as system stability and robustness, and showing its efficacy over PID and LQR. All three controllers demonstrate similar tracking performance in simulations and experiments. In steady state, the maximal pitch deviation for the PID controller is 0.075 rad, for the LQR, it is 0.025 rad, and for the MPC, it is 0.04 rad. The maximum pitch deviation for the PID-based controller is 0.3 rad after the take-off impulse, 0.06 rad for the LQR, and 0.17 rad for the MPC.
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Sha’aban, Yusuf Abubakar. "The Effect of Dead-Time and Damping Ratio on the Relative Performance of MPC and PID on Second Order Systems." Applied Sciences 13, no. 2 (January 14, 2023): 1138. http://dx.doi.org/10.3390/app13021138.
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Most industrial processes are regulated using PID control. However, many such processes often operate far from optimally because PID may not be the most suitable control method. Moreover, second-order models represent a large class of all controlled systems. This work studies the performance of some commonly used industrial PID controllers relative to MPC to understand when it is more suitable to use Model predictive control. MPC is used for this comparison because it has been the most successful industrial controller after PID. It can be concluded from the studies that improved performance can be achieved with MPC, even for modest dead time and when the damping ratio is relatively low. These improvements are prominent for dead-time dominant systems, whose dead-time to time-constant ratio is at least three.
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Okulski, Michał, and Maciej Ławryńczuk. "How Much Energy Do We Need to Fly with Greater Agility? Energy Consumption and Performance of an Attitude Stabilization Controller in a Quadcopter Drone: A Modified MPC vs. PID." Energies 15, no. 4 (February 14, 2022): 1380. http://dx.doi.org/10.3390/en15041380.
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Increasing demand for faster and more agile Unmanned Aerial Vehicles (UAVs, drones) is observed in many scenarios, including but not limited to medical supply or Search-and-Rescue (SAR) missions. Exceptional maneuverability is critical for robust obstacle avoidance during autonomous flights. A novel modification to the Model Predictive Controller (MPC) is proposed, which drastically improves the speed of the attitude controller of our quadcopter drone. The modified MPC is suitable for the onboard microcontroller and the 400 Hz main control loop. The peak and total energy consumption and the performance of the attitude controllers are assessed: the modified MPC and the default Proportional-Integral-Derivative (PID). The tests were conducted in a custom-implemented Flight Mode in the ArduCopter software stack, securing the drone in a test harness, which guarantees the experiments are repetitive. The ultimate MPC greatly increases maneuverability of the drone and may inspire more research related to faster obstacle avoidance and new types of hybrid attitude controllers to balance the agility and the power consumption.
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Cubillos Varela, Alfonso, and Oscar Barrero Mendoza. "Design and implementation of a strategy of predictive control of paddy rice drying." Revista Facultad de Ingeniería Universidad de Antioquia, no. 56 (February 28, 2013): 78–86. http://dx.doi.org/10.17533/udea.redin.14655.
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In this paper we present a Model Predictive Controller (MPC) implemented in a rice drying process with inclined silo. A pilot system is used at scale of 1:10, and capacity of 25 kg. In this system temperature and humidity of the rice grains are measured as well as air humidity. Additionally, the flow and temperature of drying air are controlled and measured. In order to design the controller a model based on thin layer and physical first principles is obtained and validated. Then, a MPC controller is designed based on this model. The type of MPC controller used is Dynamic Matrix Control (DMC) which uses the system step and free responses to estimate the next optimal control input. As a result, a good performance of the controller is obtained, showing that this is a promising technology that can be used in the Paddy rice drying process. The results show improvements in: a) reduction in drying time and energy consumption, b) over drying is avoided, c) the drying process is homogeneous, hence the quality of rice grains is better. Consequently, a positive impact on the productivity of the milling regional industry can be reached.
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Du, Xian, Ying Qing Guo, and Xiao Lei Chen. "Multivariable Constrained Predictive Control and its Application to a Commercial Turbofan Engine." Advanced Materials Research 909 (March 2014): 281–87. http://dx.doi.org/10.4028/www.scientific.net/amr.909.281.
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Aeroengine controller is a crucial and complex component aimed at pursing optimal performance while satisfying all kinds of physical and operational constrains. A novel control technique, multivariate constrained predictive control based on linear state space model, is applied to a commercial turbofan engine. To be specific, according to the control requirements of turbofan engine, its outputs are classified into two types, controlled or constrained; they are then incorporated into the cost function and the inequality constraint condition of baseline MPC algorithm respectively. Further, since key parameters of MPC play an important role in obtaining optimal performance, their selection is studied. It is shown that control performance using improved MPC is better than that of PID controller for a big disturbance while maintaining within prescribed constrains.
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Papadimitrakis, Myron, Marios Stogiannos, Haralambos Sarimveis, and Alex Alexandridis. "Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions." Sensors 21, no. 21 (October 20, 2021): 6959. http://dx.doi.org/10.3390/s21216959.
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The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel.
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S., Kanthalakshmi, and Wincy Pon Annal A. S. "An Experimental Validation of Model Based Control Techniques for Interacting Nonlinear Systems." DESIGN, CONSTRUCTION, MAINTENANCE 3 (December 31, 2023): 285–92. http://dx.doi.org/10.37394/232022.2023.3.28.
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Model based controllers are those controllers that has gained significant attention in the arena of nonlinear process control. Conical tank is a nonlinear process whose nonlinearity increases when it interacts with another conical tank. Maintaining the level of an interacting nonlinear process operating with constraints is the control objective of this paper. Model Predictive Control (MPC) has the capability of handling constraints and exerts a control action with optimization. MPC is employed for this process and the experimental results obtained are subjected to time domain analysis and the performances are compared with the performance of Proportional’, ’Integral’, ’Derivative (PID) and Internal Model controller based PID (IMC’, ’PID) controller.
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Jeong, Yonghwan, and Seongjin Yim. "Model Predictive Control-Based Integrated Path Tracking and Velocity Control for Autonomous Vehicle with Four-Wheel Independent Steering and Driving." Electronics 10, no. 22 (November 16, 2021): 2812. http://dx.doi.org/10.3390/electronics10222812.
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This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to the multiple actuators. The proposed algorithm has two modules: reference state decision and MPC-based vehicle motion controller. Reference state decision module determines reference state profiles consisting of yaw rate and velocity in order to overcome the limitation of the error dynamics-based path tracking controller, which requires several assumptions on the reference path. The MPC-based vehicle motion controller is designed with a linear time-varying vehicle model in order to optimally allocate the control effort to each actuator. A linear time-varying MPC is adopted to reduce computational burden caused by using a non-linear one. The effectiveness of the proposed algorithm is validated via simulation on MATLAB/Simulink and CarSim. The simulation results show that the proposed algorithm improves the reference tracking performance by effectively distributing the control effort to the steering angle and driving force of each actuator.
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Liang, Wei, Sizhe Ma, Erica Cochran, and Katherine A. Flanigan. "Distributed MPC-ILC Thermal Control Design for Large-Scale Multi-Zone Building HVAC System." ACM SIGEnergy Energy Informatics Review 3, no. 2 (June 2023): 34–46. http://dx.doi.org/10.1145/3607114.3607118.
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With building heating, ventilation, and air conditioning (HVAC) systems accounting for 50% the energy consumption in the building sector in the United States, there is a need to develop and implement optimal control strategies for building HVAC systems that reduce energy consumption while achieving thermal comfort for users. In the author's previous work, an integrated model predictive control (MPC) and iterative learning control (ILC) design approach was presented that took advantage of both controllers. It did not rely on model accuracy compared to conventional MPC and reduced the learning curve compared to conventional ILC. Albeit the previous numerical results showed fast convergence in most of the VAV subsystems, the gap between room air temperature and its set point remains noticeable in several zones. This paper further proposes an approach to extend the centralized MPC-ILC controller to take into account the distributed factor and the spatial distribution of the thermal zones of the VAV system. The improved control strategy allows all VAVs to interact with each other and contribute collectively to the overall convergence of the whole system. The proposed controller is implemented on a thirty-two zone VAV reheat system and compared with different controllers, including our previous MPC-ILC design. The outcomes show that the proposed controller results in faster and closer convergence among all zones even when the number of subsystems is large.