Journal articles: 'Nonlinear Model Predictive Control - NMPC'

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Al Younes, Younes, and Martin Barczyk. "Nonlinear Model Predictive Horizon for Optimal Trajectory Generation." Robotics 10, no. 3 (July 14, 2021): 90. http://dx.doi.org/10.3390/robotics10030090.

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This paper presents a trajectory generation method for a nonlinear system under closed-loop control (here a quadrotor drone) motivated by the Nonlinear Model Predictive Control (NMPC) method. Unlike NMPC, the proposed method employs a closed-loop system dynamics model within the optimization problem to efficiently generate reference trajectories in real time. We call this approach the Nonlinear Model Predictive Horizon (NMPH). The closed-loop model used within NMPH employs a feedback linearization control law design to decrease the nonconvexity of the optimization problem and thus achieve faster convergence. For robust trajectory planning in a dynamically changing environment, static and dynamic obstacle constraints are supported within the NMPH algorithm. Our algorithm is applied to a quadrotor system to generate optimal reference trajectories in 3D, and several simulation scenarios are provided to validate the features and evaluate the performance of the proposed methodology.

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Fnadi, Mohamed, and Julien Alexandre dit Sandretto. "Experimental Validation of a Guaranteed Nonlinear Model Predictive Control." Algorithms 14, no. 8 (August 20, 2021): 248. http://dx.doi.org/10.3390/a14080248.

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This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several constraints which are crucial for the system’s safety and stability, namely the state and the control limits. The proposed controller consists of two steps: filtering and branching procedures enabling to find the input intervals that fulfill the state constraints and ensure the convergence to the reference set. Then, the optimization procedure allows for computing the optimal and punctual control input that must be sent to the system’s actuators for the pendulum stabilization. The validated NMPC capabilities are illustrated through several simulations under the DynIbex library and experiments using an inverted pendulum.

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Jung, Sooyong, and John T. Wen. "Nonlinear Model Predictive Control for the Swing-Up of a Rotary Inverted Pendulum." Journal of Dynamic Systems, Measurement, and Control 126, no. 3 (September 1, 2004): 666–73. http://dx.doi.org/10.1115/1.1789541.

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This paper presents the experimental implementation of a gradient-based nonlinear model predictive control (NMPC) algorithm to the swing-up control of a rotary inverted pendulum. The key attribute of the NMPC algorithm used here is that it only seeks to reduce the error at the end of the prediction horizon rather than finding the optimal solution. This reduces the computation load and allows real-time implementation. We discuss the implementation strategy and experimental results. In addition to NMPC based swing-up control, we also present results from a gradient based iterative learning control, which is the basis our NMPC algorithm.

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Minh, Vu Trieu. "Nonlinear Model Predictive Controller and Feasible Path Planning for Autonomous Robots." Open Computer Science 6, no. 1 (November 15, 2016): 178–86. http://dx.doi.org/10.1515/comp-2016-0015.

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AbstractThis paper develops the nonlinear model predictive control (NMPC) algorithm to control autonomous robots tracking feasible paths generated directly from the nonlinear dynamic equations.NMPC algorithm can secure the stability of this dynamic system by imposing additional conditions on the open loop NMPC regulator. The NMPC algorithm maintains a terminal constrained region to the origin and thus, guarantees the stability of the nonlinear system. Simulations show that the NMPC algorithm can minimize the path tracking errors and control the autonomous robots tracking exactly on the feasible paths subject to the system’s physical constraints.

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Wei, Yao, Yanjun Wei, Yening Sun, Hanhong Qi, and Mengyuan Li. "An Advanced Angular Velocity Error Prediction Horizon Self-Tuning Nonlinear Model Predictive Speed Control Strategy for PMSM System." Electronics 10, no. 9 (May 10, 2021): 1123. http://dx.doi.org/10.3390/electronics10091123.

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In nonlinear model predictive control (NMPC), higher accuracy can be obtained with a shorter prediction horizon in steady-state, better dynamics can be obtained with a longer prediction horizon in a transient state, and calculation burden is proportional to the prediction horizon which is usually pre-selected as a constant according to dynamics of the system with NMPC. The minimum calculation and prediction accuracy are hard to ensure for all operating states. This can be improved by an online changing prediction horizon. A nonlinear model predictive speed control (NMPSC) with advanced angular velocity error (AAVE) prediction horizon self-tuning method has been proposed in which the prediction horizon is improved as a discrete-time integer variable and can be adjusted during each sampling period. A permanent magnet synchronous motor (PMSM) rotor position control system with the proposed strategy is accomplished. Tracking performances including rotor position Integral of Time-weighted Absolute value of the Error (ITAE), the maximal delay time, and static error are improved about 15.033%, 23.077%, and 10.294% respectively comparing with the conventional NMPSC strategy with a certain prediction horizon. Better disturbance resisting performance, lower weighting factor sensitivities, and higher servo stiffness are achieved. Simulation and experimental results are given to demonstrate the effectiveness and correctness.

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Bai, Guoxing, Yu Meng, Li Liu, Weidong Luo, Qing Gu, and Li Liu. "Review and Comparison of Path Tracking Based on Model Predictive Control." Electronics 8, no. 10 (September 23, 2019): 1077. http://dx.doi.org/10.3390/electronics8101077.

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Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

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Thangavel, Sakthi, Radoslav Paulen, and Sebastian Engell. "Robust Multi-Stage Nonlinear Model Predictive Control Using Sigma Points." Processes 8, no. 7 (July 16, 2020): 851. http://dx.doi.org/10.3390/pr8070851.

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We address the question of how to reduce the inevitable loss of performance that is incurred by robust multi-stage NMPC due to the lack of knowledge compared to the case where the exact plant model (no uncertainty) is available. Multi-stage NMPC in the usual setting over-approximates a continuous parametric uncertainty set by a box and includes the corners of the box and the center point into the scenario tree. If the uncertainty set is not a box, this augments the uncertainty set and results in a performance loss. In this paper, we propose to mitigate this problem by two different approaches where the scenario tree of the multi-stage NMPC is built using sigma points. The chosen sigma points help to capture the true mean and covariance of the uncertainty set more precisely. The first method computes a box over-approximation of the reachable set of the system states whereas the second method computes a box over-approximation of the reachable set of the constraint function using the unscented transformation. The advantages of the proposed schemes over the traditional multi-stage NMPC are demonstrated using simulation studies of a simple semi-batch reactor and a more complex industrial semi-batch polymerization reactor benchmark example.

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Keighobadi, J., J. Faraji, and S. Rafatnia. "Chaos Control of Atomic Force Microscope System Using Nonlinear Model Predictive Control." Journal of Mechanics 33, no. 3 (September 13, 2016): 405–15. http://dx.doi.org/10.1017/jmech.2016.89.

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AbstractOwing to robust and optimal specification, model predictive control method has received wide attentions over recent years. Since in certain operational conditions, an Atomic/scanning Force Microscope (AFM) shows chaos behavior, the chaos feedback control of the AFM system is considered. According to the nonlinear model of forces interacting between the tip of micro cantilever and the substrate of AFM; the nonlinear control methods are proposed. In the paper, the chaos control of a micro cantilever AFM based on the nonlinear model predictive control (NMPC) technique is presented. Through software simulation results, the effectiveness of the designed NMPC of the AFM is assessed. The simulation results together with analytical stability proofs indicate that the proposed method is effective in keeping the system in a stable range.

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Isaza Hurtado, Jhon Alexander, Diego A. Muñoz, and Hernán Álvarez. "Efficient solution of nonlinear model predictive control by a restricted enumeration method." Enfoque UTE 9, no. 4 (December 21, 2018): 13–23. http://dx.doi.org/10.29019/enfoqueute.v9n4.393.

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This work presents an alternative method to solve the nonlinear program (NLP) for nonlinear model predictive control (NMPC) problems. The NLP is the most computational demanding task in NMPC, which limits the industrial implementation of this control strategy. Therefore, it is important to consider algorithms that can solve the nonlinear program, not only in real time but also guaranteeing feasibility. In this work, the restricted enumeration method is proposed as alternative to solve the NLP for NMPC problems, showing successful results for pH control in a sugar cane process plant. This method enumerates in restricted way a set of final control element possible positions around the current one. Next, it tests all positions in that set to find the best one, taken as the optimization solution.

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Dettori, Stefano, Alessandro Maddaloni, Filippo Galli, Valentina Colla, Federico Bucciarelli, Damaso Checcacci, and Annamaria Signorini. "Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control." Energies 14, no. 13 (July 2, 2021): 3998. http://dx.doi.org/10.3390/en14133998.

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The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.

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Fontes, C. H., and M. J. Mendes. "Nonlinear predictive control of an industrial slurry reactor." Sba: Controle & Automação Sociedade Brasileira de Automatica 19, no. 4 (December 2008): 417–30. http://dx.doi.org/10.1590/s0103-17592008000400005.

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A nonlinear model predictive control (NMPC) is applied to a slurry polymerization stirred tank reactor for the production of high-density polyethylene. Its performance is examined to reach the required mean molecular weight and comonomer composition, together with the temperature setpoint. A complete phenomenological model including the microscale, the mesoscale and the macroscale levels was developed to represent the plant. The control algorithm comprises a neural dynamic model that uses a neural network structure with a feedforward topology. The algorithm implementation considers the optimization problem, the manipulated and controlled variables adopted and presents results for the regulatory and servo problems, including the possibility of dead time and multi-rate sampling in the controlled variables. The simulation results show the high performance of the NMPC algorithm based in a model for one-step ahead prediction only, and, at the same time, attests the strong difficulty to control polymer properties with dead time in their measurements.

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Dolatkhah, Somayeh, and Mohamad Bagher Menhaj. "Model Predictive Control Based on Real Time Particle Swarm Optimization (IPO)." Advanced Materials Research 403-408 (November 2011): 3461–68. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3461.

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A novel approach for the implementation of nonlinear model predictive control (NMPC) is proposed based on Individual particle optimizer (IPO1) while functional link neural network (FLNN) is introduced as a nonlinear model of the plant where individual particle optimization (IPO2) is applied for training of the neural network. The IPO algorithm is used as a real-time optimal tuning technique, which is applied to the neural network so that the proposed optimized FLNN can be used in nonlinear model predictive control scheme. Finally, the proposed NMPC applied to the Load frequency control (LFC) problem. Simulation results verify that the proposed IPO based technique possesses efficient performance in the sense of speed up and set point tracking.).

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Zhu, Hongxia, Gang Zhao, Li Sun, and Kwang Y. Lee. "Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm." Sustainability 11, no. 18 (September 18, 2019): 5102. http://dx.doi.org/10.3390/su11185102.

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This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

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Nederkoorn, Eelco, Jan Schuurmans, Joep Grispen, and Wytze Schuurmans. "Continuous nonlinear model predictive control of a hybrid water system." Journal of Hydroinformatics 15, no. 2 (December 13, 2012): 246–57. http://dx.doi.org/10.2166/hydro.2012.168.

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Incorporating weather forecasts in the control of land surface water levels requires predictions of the net inflow to the water system. This net inflow is the combined flow of an incoming load (rain, evaporation, etc.) and outgoing pump rates. Because the pump costs are considerable, optimal pump schedules have minimal energy consumption. Model predictive control (MPC) is able to compute, revise and apply such optimized schedules by incorporating a model of the water system. The pumps typically cause discontinuities in the model, which leads to mathematical complications. Avoiding advanced solving techniques for these hybrid systems, this paper introduces an alternative that enables pure continuous MPC by smoothing the jumps. Although the resulting underlying model is continuous, it is also highly nonlinear. This requires use of the specialized class of nonlinear model predictive control (NMPC), which is able to cope with the arising nonlinearities. Control inputs computed by these methods can be translated to the original hybrid system by a final post-processing step. This paper presents the outlined scheme, and verifies it by applying an optimized NMPC implementation (the DotX nonlinear predictive controller, DNPC), equipped with the approximated continuous nonlinear model, to a real-life hybrid water system.

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Grigorescu, Sorin, Cosmin Ginerica, Mihai Zaha, Gigel Macesanu, and Bogdan Trasnea. "LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles." International Journal of Advanced Robotic Systems 18, no. 3 (May 1, 2021): 172988142110195. http://dx.doi.org/10.1177/17298814211019544.

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In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

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Chu, Zhenzhong, Da Wang, and Fei Meng. "An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles." Machines 9, no. 5 (May 20, 2021): 105. http://dx.doi.org/10.3390/machines9050105.

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An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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Ren, Zhengru, Roger Skjetne, and Zhen Gao. "A Crane Overload Protection Controller for Blade Lifting Operation Based on Model Predictive Control." Energies 12, no. 1 (December 24, 2018): 50. http://dx.doi.org/10.3390/en12010050.

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Lifting is a frequently used offshore operation. In this paper, a nonlinear model predictive control (NMPC) scheme is proposed to overcome the sudden peak tension and snap loads in the lifting wires caused by lifting speed changes in a wind turbine blade lifting operation. The objectives are to improve installation efficiency and ensure operational safety. A simplified three-dimensional crane-wire-blade model is adopted to design the optimal control algorithm. A crane winch servo motor is controlled by the NMPC controller. The direct multiple shooting approach is applied to solve the nonlinear programming problem. High-fidelity simulations of the lifting operations are implemented based on a turbulent wind field with the MarIn and CaSADi toolkit in MATLAB. By well-tuned weighting matrices, the NMPC controller is capable of preventing snap loads and axial peak tension, while ensuring efficient lifting operation. The performance is verified through a sensitivity study, compared with a typical PD controller.

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Lee, Taekgyu, and Yeonsik Kang. "Performance Analysis of Deep Neural Network Controller for Autonomous Driving Learning from a Nonlinear Model Predictive Control Method." Electronics 10, no. 7 (March 24, 2021): 767. http://dx.doi.org/10.3390/electronics10070767.

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Nonlinear model predictive control (NMPC) is based on a numerical optimization method considering the target system dynamics as constraints. This optimization process requires large amount of computation power and the computation time is often unpredictable which may cause the control update rate to overrun. Therefore, the performance must be carefully balanced against the computational time. To solve the computation problem, we propose a data-based control technique based on a deep neural network (DNN). The DNN is trained with closed-loop driving data of an NMPC. The proposed "DNN control technique based on NMPC driving data" achieves control characteristics comparable to those of a well-tuned NMPC within a reasonable computation period, which is verified with an experimental scaled-car platform and realistic numerical simulations.

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Wan, Jiao Na, Ju Wen Zhang, Zhi Qiang Wang, Tie Jun Zhang, and Ke Xin Wang. "Analysis of NMPC Algorithm Based on Reduced Precision Solution Criteria." Applied Mechanics and Materials 325-326 (June 2013): 1282–89. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1282.

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This paper studies the stability of nonlinear model predictive control (NMPC) based on sub-optimal solution obtained under reduced precision solution (RPS) criteria. NMPC needs to solve the optimal control problem (OCP) quickly and the input is injected into the controlled plant in time. Traditional convergence criteria in optimization algorithms usually cost excessive long computation time with little improvement of solution, which results in degradation of control performance eventually. RPS criteria are new convergence criteria for deciding whether the current iterate is good enough and whether the optimization procedure should be terminated. It can terminate the optimization process timely. This work gives the proof of the rps-NMPCs property. Simulations are done to analyze the effect of disturbance, especially when computational delay exists, on the closed-loop system controlled by rps-NMPC, and demonstrate that the algorithm owns good stability when disturbance exists.

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Bakaráč, Peter, and Michal Kvasnica. "Fast nonlinear model predictive control of a chemical reactor: a random shooting approach." Acta Chimica Slovaca 11, no. 2 (October 1, 2018): 175–81. http://dx.doi.org/10.2478/acs-2018-0025.

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Abstract This paper presents a fast way of implementing nonlinear model predictive control (NMPC) using the random shooting approach. Instead of calculating the optimal control sequence by solving the NMPC problem as a nonlinear programming (NLP) problem, which is time consuming, a sub-optimal, but feasible, sequence of control inputs is determined randomly. To minimize the induced sub-optimality, numerous random control sequences are selected and the one that yields the smallest cost is selected. By means of a motivating case study we demonstrate that the random shooting-based approach is superior, from a computational point of view, to state-of-the-art NLP solvers, and features a low level of sub-optimality. The case study involves a continuous stirred tank reactor where a fast multi-component chemical reaction takes place.

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Wang, Long Sheng, and Hong Ze Xu. "Nonlinear Model Predictive Control for Automatic Train Operation with Actuator Saturation and Speed Limit." Applied Mechanics and Materials 678 (October 2014): 377–81. http://dx.doi.org/10.4028/www.scientific.net/amm.678.377.

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This paper addresses a position and speed tracking problem for high-speed train automatic operation with actuator saturation and speed limit. A nonlinear model predictive control (NMPC) approach, which allows the explicit consideration of state and input constraints when formulating the problem and is shown to guarantee the stability of the closed-loop system by choosing a proper terminal cost and terminal constraints set, is proposed. In NMPC, a cost function penalizing both the train position and speed tracking error and the changes of tracking/braking forces will be minimized on-line. The effectiveness of the proposed approach is verified by numerical simulations.

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Marusak, Piotr M. "Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms." Algorithms 14, no. 1 (January 17, 2021): 25. http://dx.doi.org/10.3390/a14010025.

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A method for the advanced construction of the dynamic matrix for Model Predictive Control (MPC) algorithms with linearization is proposed in the paper. It extends numerically efficient fuzzy algorithms utilizing skillful linearization. The algorithms combine the control performance offered by the MPC algorithms with nonlinear optimization (NMPC algorithms) with the numerical efficiency of the MPC algorithms based on linear models in which the optimization problem is a standard, easy-to-solve, quadratic programming problem with linear constraints. In the researched algorithms, the free response obtained using a nonlinear process model and the future trajectory of the control signals is used to construct an advanced dynamic matrix utilizing the easy-to-obtain fuzzy model. This leads to obtaining very good prediction and control quality very close to those offered by NMPC algorithms. The proposed approach is tested in the control system of a nonlinear chemical control plant—a CSTR reactor with the van de Vusse reaction.

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Merabti, Halim, and Khaled Belarbi. "Accelerated micro particle swarm optimization for the solution of nonlinear model predictive control." World Journal of Engineering 14, no. 6 (December 4, 2017): 509–21. http://dx.doi.org/10.1108/wje-01-2017-0004.

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Purpose Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AµPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one. Design/methodology/approach The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance. Findings The results of the experiments are very promising and show that AµPSO can be considered as an alternative to the classical solution methods. Originality/value The computation time is less than 0.02 ms using an Intel Core i7 with 8GB of RAM.

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Huang, Yan-Shu, M. Ziyan Sheriff, Sunidhi Bachawala, Marcial Gonzalez, Zoltan K. Nagy, and Gintaras V. Reklaitis. "Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press." Processes 9, no. 9 (September 8, 2021): 1612. http://dx.doi.org/10.3390/pr9091612.

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The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

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Garcia, Gonzalo, Shahriar Keshmiri, and Thomas Stastny. "Nonlinear model predictive controller robustness extension for unmanned aircraft." International Journal of Intelligent Unmanned Systems 3, no. 2/3 (May 11, 2015): 93–121. http://dx.doi.org/10.1108/ijius-01-2015-0002.

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Purpose – Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics. However, these predictive controllers do not perform robustly in the presence of physics-based model mismatches and uncertainties. Unmodeled dynamics and external disturbances are unpredictable and unsteady, which can dramatically degrade predictive controllers’ performance. To address this limitation, the purpose of this paper is to propose a new systematic approach using frequency-dependent weighting matrices. Design/methodology/approach – In this framework, frequency-dependent weighting matrices jointly minimize closed-loop sensitivity functions. This work presents the first practical implementation where the frequency content information of uncertainty and disturbances is used to provide a significant degree of robustness for a time-domain nonlinear predictive controller. The merit of the proposed method is successfully verified through the design, coding, and numerical implementation of a robust nonlinear model predictive controller. Findings – The proposed controller commanded and controlled a large unmanned aerial system (UAS) with unsteady and nonlinear dynamics in the presence of environmental disturbances, measurement bias or noise, and model uncertainties; the proposed controller robustly performed disturbance rejection and accurate trajectory tracking. Stability, performance, and robustness are attained in the NMPC framework for a complex system. Research limitations/implications – The theoretical results are supported by the numerical simulations that illustrate the success of the presented technique. It is expected to offer a feasible robust nonlinear control design technique for any type of systems, as long as computational power is available, allowing a much larger operational range while keeping a helpful level of robustness. Robust control design can be more easily expanded from the usual linear framework, allowing meaningful new experimentation with better control systems. Originality/value – Such algorithms allows unstable and unsteady UASs to perform reliably in the presence of disturbances and modeling mismatches.

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Trigkas, Dimitrios, Chrysovalantou Ziogou, Spyros Voutetakis, and Simira Papadopoulou. "Virtual Energy Storage in RES-Powered Smart Grids with Nonlinear Model Predictive Control." Energies 14, no. 4 (February 18, 2021): 1082. http://dx.doi.org/10.3390/en14041082.

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The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weather conditions and load requests, demonstrates the ability of the NMPC to supervise the multi-node microgrid resulting to optimal energy management and reduction of the auxiliary power devices operation.

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Fallah, Zeinab, Mojtaba Ahmadieh Khanesar, and Mohammad Teshnehlab. "Design of a hierarchical fuzzy model predictive controller." International Journal of Engineering & Technology 4, no. 2 (April 15, 2015): 342. http://dx.doi.org/10.14419/ijet.v4i2.2854.

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In order to control a nonlinear system using Nonlinear Model Predictive Control (NMPC), a nonlinear model from system is required. In this paper, a hierarchical neuro-fuzzy model is used for nonlinear identification of the plant. The use of hierarchical neuro-fuzzy systems makes it possible to overcome the curse of dimensionality. In neuro-fuzzy systems, if the input number increases, then the number of rules increases exponentially. One solution to this problem is making use of Hierarchical Fuzzy System Mamdani (HFS) in which the number of the rules increases linearly. Gradient descent and recursive least square algorithm are used simultaneously to train the parameters of the HFS. Gradient Descent Algorithm is utilized to train the parameters, which appear nonlinearly in the output of HFS, and RLS is used to train the parameters of consequent the part, which appears linearly in the output of HFS. Finally, a model predictive fuzzy controller based on a predictive cost function is proposed. Using Gradient Descent Algorithm, the parameters of the controller are optimized. The proposed controller is simulated on the control of continuous stirred tank reactor. It is shown that the proposed method can control the system with high performance.

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Degachi, Hajer, Bechir Naffeti, Wassila Chagra, and Moufida Ksouri. "Filled Function Method for Nonlinear Model Predictive Control." Mathematical Problems in Engineering 2018 (June 4, 2018): 1–8. http://dx.doi.org/10.1155/2018/9497618.

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A new method is used to solve the nonconvex optimization problem of the nonlinear model predictive control (NMPC) for Hammerstein model. Using nonlinear models in MPC leads to a nonlinear and nonconvex optimization problem. Since control performances depend essentially on the results of the optimization method, in this work, we propose to use the filled function as a global optimization method to solve the nonconvex optimization problem. Using this method, the control law can be obtained through two steps. The first step consists of determining a local minimum of the objective function. In the second step, a new function is constructed using the local minimum of the objective function found in the first step. The new function is called the filled function; the new constructed function allows us to obtain an initialization near the global minimum. Once this initialization is determined, we can use a local optimization method to determine the global control sequence. The efficiency of the proposed method is proved firstly through benchmark functions and then through the ball and beam system described by Hammerstein model. The results obtained by the presented method are compared with those of the genetic algorithm (GA) and the particle swarm optimization (PSO).

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Hu, Yingbai, Hang Su, Longbin Zhang, Shu Miao, Guang Chen, and Alois Knoll. "Nonlinear Model Predictive Control for Mobile Robot Using Varying-Parameter Convergent Differential Neural Network." Robotics 8, no. 3 (July 31, 2019): 64. http://dx.doi.org/10.3390/robotics8030064.

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The mobile robot kinematic model is a nonlinear affine system, which is constrained by velocity and acceleration limits. Therefore, the traditional control methods may not solve the tracking problem because of the physical constraint. In this paper, we present the nonlinear model predictive control (NMPC) algorithm to track the desired trajectory based on neural-dynamic optimization. In the proposed algorithm, the NMPC scheme utilizes a new neural network named the varying-parameter convergent differential neural network (VPCDNN) which is a Hopfifield-neural network structure with respect to the differential equation theory to solve the quadratic programming (QP) problem. The new network structure converges to the global optimal solution and it is more efficient than traditional numerical methods. In the simulation, we verify that the proposed method is able to successfully track reference trajectories with a two-wheel mobile robot. The experimental validation has been conducted in simulation and the results show that the proposed method is able to precisely track the trajectory maintaining a high robustness based on the VPCDNN solver.

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Löw, Stefan, and Dragan Obradovic. "Echtzeit-Implementierung von NMPC." atp magazin 60, no. 08 (August 20, 2018): 46–53. http://dx.doi.org/10.17560/atp.v60i08.2359.

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Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

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Löw, Stefan, and Dragan Obradovic. "Echtzeit-Implementierung von NMPC." atp edition 60, no. 08 (August 20, 2018): 46. http://dx.doi.org/10.17560/atp.v60i09.2359.

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Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

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32

Abdul Hamid, Umar Zakir, Hairi Zamzuri, Tsuyoshi Yamada, Mohd Azizi Abdul Rahman, Yuichi Saito, and Pongsathorn Raksincharoensak. "Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoidance system with move blocking strategy." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, no. 10 (October 24, 2017): 1353–73. http://dx.doi.org/10.1177/0954407017729057.

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The collision avoidance (CA) system is a pivotal part of the autonomous vehicle. Ability to navigate the vehicle in various hazardous scenarios demands reliable actuator interventions. In a complex CA scenario, the increased nonlinearity requires a dependable control strategy. For example, during collisions with a sudden appearing obstacle (i.e. crossing pedestrian, vehicle), the abrupt increment of vehicle longitudinal and lateral forces summation during the CA maneuver demands a system with the ability to handle coupled nonlinear dynamics. Failure to address the aforementioned issues will result in collisions and near-miss incidents. Thus, to solve these issues, a nonlinear model predictive control (NMPC)-based path tracking strategy is proposed as the automated motion guidance for the host vehicle CA architecture. The system is integrated with the artificial potential field (APF) as the motion planning strategy. In a hazardous scenario, APF measures the collision risks and formulates the desired yaw rate and deceleration metrics for the path replanning. APF ensures an optimal replanned trajectory by including the vehicle dynamics into its optimization formulation. NMPC then acts as the coupled path and speed tracking controller to enable vehicle navigation. To accommodate vehicle comfort during the avoidance, NMPC is constrained. Due to its complexity as a nonlinear controller, NMPC can be time-consuming. Therefore, a move blocking strategy is assimilated within the architecture to decrease the system’s computational burden. The modular nature of the architecture allows each strategy to be tuned and developed independently without affecting each others’ performance. The system’s tracking performance is analyzed by computational simulations with several CA scenarios (crossing pedestrian, parked bus, and sudden appearing moving vehicle at an intersection). NMPC tracking performance is compared to the nominal MPC and linear controllers. The effect of move blocking strategies on NMPC performance are analyzed, and the results are compared in terms of mean squared error values. The inclusion of nonlinear tracking controllers in the architecture is shown to provide reliable CA actions in various hazardous scenarios. The work is important for the development of a reliable controller strategy for multi-scenario CA of the fully autonomous vehicle.

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Mehndiratta, Mohit, and Erdal Kayacan. "Receding horizon control of a 3 DOF helicopter using online estimation of aerodynamic parameters." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 8 (April 17, 2017): 1442–53. http://dx.doi.org/10.1177/0954410017703414.

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This study presents a numerical implementation of fast nonlinear model predictive control (NMPC) and nonlinear moving horizon estimation (NMHE) for the trajectory tracking problem of a 3 degree of freedom (DOF) helicopter. The motivation behind using the NMPC instead of its linear counterpart is that the helicopter is operated over nonlinear regions. Moreover, this system has cross-couplings that make the control of the system even more complicated. What is more, according to our simulation scenario, the system has a time-varying dynamical model because it has time-varying parameters which are estimated online using NMHE and the extended Kalman filter (EKF) throughout the control. Although NMHE is computationally more demanding, its capability of incorporating the constraints encourages us to utilize NMHE rather than EKF. Two reference trajectories, namely, sinusoidal and square-like, are tracked, and owing to the better learning capability of NMHE over EKF, the NMPC-NMHE closed-loop control framework is able to track both reference signals with more accuracy than the NMPC-EKF control framework, even under parameter uncertainties. Thanks to the ACADO toolkit, the combined average execution time is 4 milliseconds, demonstrating the potential of the proposed framework for real-time aerospace applications using relatively cheaper processors.

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Murilo, André, and Renato Vilela Lopes. "Unified NMPC framework for attitude and position control for a VTOL UAV." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, no. 7 (May 13, 2019): 889–903. http://dx.doi.org/10.1177/0959651819847053.

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In this article, a parameterized nonlinear model predictive control strategy is developed for trajectory tracking and stabilization of a quadrotor unmanned aerial vehicle. The control strategy handles structurally with constraints on commands and input derivatives and deals with model uncertainties and white noise on whole state vector. Moreover, the resulting parametrization decreases the number of decision variables related to the nonlinear optimization problem substantially, which highly facilitates the attainment of the optimal control sequence. The proposed nonlinear model predictive control framework also enables the use of different nonlinear formulations of the unmanned aerial vehicle whatever the complexity of the model being used. Simulation results for different flight conditions are presented in order to show the efficiency of the tracking performance and highlight the advantages of the proposed control scheme.

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Levermann, Philipp, Fabian Freiberger, Uma Katha, Henning Zaun, Johannes Möller, Volker C. Hass, Karl Michael Schoop, Jürgen Kuballa, and Ralf Pörtner. "NMPC-Based Workflow for Simultaneous Process and Model Development Applied to a Fed-Batch Process for Recombinant C. glutamicum." Processes 8, no. 10 (October 19, 2020): 1313. http://dx.doi.org/10.3390/pr8101313.

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For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. By using the NMPC algorithm, the process is designed with respect to a target function (product yield, biomass concentration) with a drastically decreased number of experiments. A workflow for the usage of the NMPC algorithm as a process development tool is outlined. The NMPC algorithm is capable of improving various process states, such as product yield and biomass concentration. It uses on-line and at-line data and controls and optimizes the process by model-based process extrapolation. In this study, the algorithm is applied to a Corynebacterium glutamicum process. In conclusion, the potency of the NMPC algorithm as a powerful tool for process development is demonstrated. In particular, the benefits of the system regarding the characterization and optimization of a fed-batch process are outlined. With the NMPC algorithm, process development can be run simultaneously to strain development, resulting in a shortened time to market for novel products.

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Wen, Y., L. Chen, Y. Wang, D. Sun, D. Duan, and J. Liu. "Nonlinear DOB-based explicit NMPC for station-keeping of a multi-vectored propeller airship with thrust saturation." Aeronautical Journal 122, no. 1257 (November 2018): 1753–74. http://dx.doi.org/10.1017/aer.2018.91.

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ABSTRACTA nonlinear station-keeping control method for a multi-vectored propeller airship under unknown wind field with thrust saturation is developed, which is composed of three modules: nonlinear model predictive controller (NMPC), disturbance observer (DOB) and tracking differentiator (TD). The nonlinear kinematics and dynamics models are introduced, and the wind effect is considered by the wind-induced aerodynamic force. Based on both models, an explicit NMPC is designed. Then a nonlinear DOB is introduced to estimate the wind disturbance. A TD, showing the relationship between the maximum propulsion force and the maximum flight acceleration, is proposed to handle the thrusts’ amplitude saturation. Stability analysis shows that the closed-loop system is globally asymptotically stable. Simulations for a multi-vectored propeller airship are conducted to demonstrate the robustness and effectiveness of the proposed method.

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Rybus, Tomasz, Karol Seweryn, and Jurek Z. Sasiadek. "Control System for Free-Floating Space Manipulator Based on Nonlinear Model Predictive Control (NMPC)." Journal of Intelligent & Robotic Systems 85, no. 3-4 (July 8, 2016): 491–509. http://dx.doi.org/10.1007/s10846-016-0396-2.

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Dang, Dongfang, Feng Gao, and Qiuxia Hu. "Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling." Applied Sciences 10, no. 9 (May 2, 2020): 3180. http://dx.doi.org/10.3390/app10093180.

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Vehicles are highly coupled and multi-degree nonlinear systems. The establishment of an appropriate vehicle dynamical model is the basis of motion planning for autonomous vehicles. With the development of autonomous vehicles from L2 to L3 and beyond, the automatic driving system is required to make decisions and plans in a wide range of speeds and on bends with large curvature. In order to make precise and high-quality control maneuvers, it is important to account for the effects of dynamical coupling in these working conditions. In this paper, a new single-coupled dynamical model (SDM) is proposed to deal with the various dynamical coupling effects by identifying and simplifying the complicated one. An autonomous vehicle motion planning problem is then formulated using the nonlinear model predictive control theory (NMPC) with the SDM constraint (NMPC-SDM). We validated the NMPC-SDM with hardware-in-the-loop (HIL) experiments to evaluate improvements to control performance by comparing with the planners original design, using the kinematic and single-track models. The comparative results show the superiority of the proposed motion planning algorithm in improving the maneuverability and tracking performance.

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Gutekunst, Jürgen, Robert Scholz, Armin Nurkanović, Amer Mešanović, Hans Georg Bock, and Ekaterina Kostina. "Fast moving horizon estimation using multi-level iterations for microgrid control." at - Automatisierungstechnik 68, no. 12 (November 18, 2020): 1059–76. http://dx.doi.org/10.1515/auto-2020-0081.

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AbstractAccurate state-estimation is a vital prerequisite for fast feedback control methods such as Nonlinear Model Predictive Control (NMPC). For efficient process control, it is of great importance that the estimation process is carried out as fast as possible to provide the feedback mechanism with fresh information and enable fast reactions in case of any disturbances. We discuss how Multi-Level Iterations (MLI), known from NMPC, can be applied to the Moving Horizon Estimation (MHE) method for estimating the states and parameters of a system described by a Differential Algebraic Equation model. A challenging field of application for the proposed MLI-MHE method are electric microgrids. These push current control approaches to their limits due to the rising penetration of volatile renewable energy sources and the fast electrical system dynamics. We investigate the closed-loop control performance of the proposed MLI-MHE algorithm in combination with an NMPC controller for a realistic sized microgrid as a numerical example.

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Xu, Zhongxian, Lile He, Ning He, and Lipeng Qi. "Self-Triggered Model Predictive Control for Perturbed Underwater Robot Systems." Mathematical Problems in Engineering 2021 (January 4, 2021): 1–9. http://dx.doi.org/10.1155/2021/4324389.

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Aiming at solving the control problem of a constrained and perturbed underwater robot, a control method was proposed by combining the self-triggered mechanism and the nonlinear model predictive control (NMPC). The theoretical properties of the kinematic model of the underwater robot, as well as the corresponding MPC controller, are first studied. Then, a novel technique for determining the next update moment of both the optimal control problem and the system state is developed. It is further rigorously proved that the proposed algorithm can (1) stabilize the closed-loop underwater robot system, (2) reduce the time of solving the optimal control problem and (3) save the information transfer resources. Finally, a case study is provided to show the effectiveness of the developed researched scheme.

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Tamimi, Jasem. "Modeling, simulation and control of a twin-inverted pendulum on a moving cart." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 04 (February 22, 2021): 2150027. http://dx.doi.org/10.1142/s1793962321500276.

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In this paper, a mathematical model of a twin-inverted pendulum on a moving cart has been derived. This is done using the Lagrange–Euler method and, hence, a highly nonlinear mathematical model is resulted from this derivation. These nonlinear and unstable dynamics are written in a simple matrix form. For this challenging system, we use two types of efficient control approaches to treat the control problem of the twin inverted pendulum, namely, linear quadratic regulator (LQR) and nonlinear model predictive control (NMPC). Simulations with several scenarios are also presented to demonstrate the control performances and the model validity.

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42

Zhan, Jixian, and Masaru Ishida. "The multi-step predictive control of nonlinear SISO processes with a neural model predictive control (NMPC) method." Computers & Chemical Engineering 21, no. 2 (October 1997): 201–10. http://dx.doi.org/10.1016/0098-1354(95)00257-x.

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Kafetzis, Alexandros, Chrysovalantou Ziogou, Simira Papadopoulou, Spyridon Voutetakis, and Panos Seferlis. "Nonlinear Model Predictive Control of an Autonomous Power System Based on Hydrocarbon Reforming and High Temperature Fuel Cell." Energies 14, no. 5 (March 3, 2021): 1371. http://dx.doi.org/10.3390/en14051371.

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The integration and control of energy systems for power generation consists of multiple heterogeneous subsystems, such as chemical, electrochemical, and thermal, and contains challenges that arise from the multi-way interactions due to complex dynamic responses among the involved subsystems. The main motivation of this work is to design the control system for an autonomous automated and sustainable system that meets a certain power demand profile. A systematic methodology for the integration and control of a hybrid system that converts liquefied petroleum gas (LPG) to hydrogen, which is subsequently used to generate electrical power in a high-temperature fuel cell that charges a Li-Ion battery unit, is presented. An advanced nonlinear model predictive control (NMPC) framework is implemented to achieve this goal. The operational objective is the satisfaction of power demand while maintaining operation within a safe region and ensuring thermal and chemical balance. The proposed NMPC framework based on experimentally validated models is evaluated through simulation for realistic operation scenarios that involve static and dynamic variations of the power load.

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Yao, Xuliang, Guangyi Yang, and Yu Peng. "Nonlinear Reduced-Order Observer-Based Predictive Control for Diving of an Autonomous Underwater Vehicle." Discrete Dynamics in Nature and Society 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/4394571.

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The attitude control and depth tracking issue of autonomous underwater vehicle (AUV) are addressed in this paper. By introducing a nonsingular coordinate transformation, a novel nonlinear reduced-order observer (NROO) is presented to achieve an accurate estimation of AUV’s state variables. A discrete-time model predictive control with nonlinear model online linearization (MPC-NMOL) is applied to enhance the attitude control and depth tracking performance of AUV considering the wave disturbance near surface. In AUV longitudinal control simulation, the comparisons have been presented between NROO and full-order observer (FOO) and also between MPC-NMOL and traditional NMPC. Simulation results show the effectiveness of the proposed method.

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Nurkanović, Armin, Amer Mešanović, Mario Sperl, Sebastian Albrecht, Ulrich Münz, Rolf Findeisen, and Moritz Diehl. "Optimization-based primary and secondary control of microgrids." at - Automatisierungstechnik 68, no. 12 (November 18, 2020): 1044–58. http://dx.doi.org/10.1515/auto-2020-0088.

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AbstractThis article discusses how to use optimization-based methods to efficiently operate microgrids with a large share of renewables. We discuss how to apply a frequency-based method to tune the droop parameters in order to stabilize the grid and improve oscillation damping after disturbances. Moreover, we propose a centralized real-time feasible nonlinear model predictive control (NMPC) scheme to achieve efficient frequency and voltage control while considering economic dispatch results. Centralized NMPC for secondary control is a computationaly challenging task. We demonstrate how to reduce the computational burden using the Advanced Step Real-Time Iteration with nonuniform discretization grids. This reduces the computational burden up to 60 % compared to a standard uniform approach, while having only a minor performance loss. All methods are validated on the example of a 9-bus microgrid, which is modeled with a complex differential algebraic equation.

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Gai, Jun Feng, Guo Rong Zhao, and Da Wang Zhou. "A NMPC Scheme Based on Stirling's Interpolation Formula Approximation Method." Applied Mechanics and Materials 530-531 (February 2014): 977–80. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.977.

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For a kind of nonlinear system whose input-output function is not differentiable, we proposed a model predictive control scheme based on linearization approximation method. We linearized the object nonlinear system using Stirling's interpolation formula method, and reformulated the control performance index to a quadratic optimization problem, and then, we obtained the optimization control sequences by solving the quadratic optimization problem. In order to reduce the complexity of computation, we ignored the high-order terms associated with the linearization. The simulations show that the presented NMPC scheme can achieve a satisfactory control result, also can decrease the dissipation of control energy and control time.

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Mayer, Annika, Daniel Müller, Adrian Raisch, and Oliver Sawodny. "Model-predictive reference trajectory planning for redundant pneumatic collaborative robots." at - Automatisierungstechnik 68, no. 5 (May 27, 2020): 360–74. http://dx.doi.org/10.1515/auto-2019-0127.

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AbstractCollaborative robots have the potential to simplify the working day of the future. The goal in the development of these robots is to assist human operators by handling all sorts of tasks. A common underlying problem is to move the robot’s tool center point in a desired way. In this work we consider the generation of a feasible trajectory in joint space given a reference in task space. This is done at the example of the Bionic Handling Assistant (BHA), a compliant, redundant and pneumatically driven continuum robot. The trajectory for the BHA is obtained using a model control loop (MCL) which internally realizes a nonlinear model predictive controller (NMPC). We simplify the high dimensional and nonlinear model of the BHA to a computational efficient model which still covers the major effects of the original dynamics. This results not only in a feasible trajectory but also enables the model control loop to be real-time applicable. The proposed method is validated in simulation.

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Akinola, Toluleke E., Eni Oko, Xiao Wu, Keming Ma, and Meihong Wang. "Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process." Energy 213 (December 2020): 118840. http://dx.doi.org/10.1016/j.energy.2020.118840.

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Hussain, Syed A., Babak Shahian Jahromi, and Sabri Cetin. "Cooperative Highway Lane Merge of Connected Vehicles Using Nonlinear Model Predictive Optimal Controller." Vehicles 2, no. 2 (March 25, 2020): 249–66. http://dx.doi.org/10.3390/vehicles2020014.

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Of all driving functions, one of the critical maneuvers is the lane merge. A cooperative Nonlinear Model Predictive Control (NMPC)-based optimization method for implementing a highway lane merge of two connected autonomous vehicles is presented using solutions obtained by the direct multiple shooting method. A performance criteria cost function, which is a function of the states and inputs of the system, was optimized subject to nonlinear model and maneuver constraints. An optimal formulation was developed and then solved on a receding horizon using direct multiple shooting solutions; this is implemented using an open-source ACADO code. Numerical simulation results were performed in a real-case scenario. The results indicate that the implementation of such a controller is possible in real time, in different highway merge situations.

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Quillen, Paul, and Kamesh Subbarao. "Minimum control effort–based path planning and nonlinear guidance for autonomous mobile robots." International Journal of Advanced Robotic Systems 15, no. 6 (November 1, 2018): 172988141881263. http://dx.doi.org/10.1177/1729881418812635.

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This article puts forth a framework using model-based techniques for path planning and guidance for an autonomous mobile robot in a constrained environment. The path plan is synthesized using a numerical navigation function algorithm that will form its potential contour levels based on the “minimum control effort.” Then, an improved nonlinear model predictive control approach is employed to generate high-level guidance commands for the mobile robot to track a trajectory fitted along the planned path leading to the goal. A backstepping-like nonlinear guidance law is also implemented for comparison with the NMPC formulation. Finally, the performance of the resulting framework using both nonlinear guidance techniques is verified in simulation where the environment is constrained by the presence of static obstacles.

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Journal articles on the topic 'Nonlinear Model Predictive Control - NMPC'

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