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Journal articles on the topic 'Expert model predictive controller'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Qian, Zheng Zai, Gong Cai Xin, and Jin Niu Tao. "Predictive Control Based on Fuzzy Expert PID Tuning Control." Advanced Materials Research 466-467 (February 2012): 1207–11. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1207.

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In decade years, several simple methods for the automatic tuning of PID controllers have been proposed. There have been different approaches to the problem of deriving a PID-like adaptive controller. All of these can be classified into two broad categories: model-based; or expert systems. In this paper a new expert adaptive controller is proposed in which the underlying control law is a PID structure. The design is based on the fuzzy logic and the generalized predictive control theory. The proposed controller can be applied to a large class of systems which is model uncertainty or strong non-linearity. Simulation results have also been illustrated. It shows that the proposed expert PID-like controller performed well than generally used PID.

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Rivas-Perez, SotomayorMoriano, PérezZuñiga, and Soto-Angles. "Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination." Applied Sciences 9, no. 14 (2019): 2932. http://dx.doi.org/10.3390/app9142932.

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This article addresses the design and real-time implementation of an expert model predictive controller (Expert MPC) for the control of the brackish and seawater desalination process in a pilot-scale reverse osmosis (RO) plant. This pilot-scale plant is used in order to obtain the optimal operation conditions of the RO desalination process through the implementation of different control strategies, as well as in the training of operators in the new control and management technologies. A dynamical mathematical model of this plant has been developed based on the available field data and system identification procedures. Predictions of the obtained model were in good agreement with the available field data. The designed Expert MPC is distinguished by having a plant identification block and an expert system. The expert system, using a rule-based approach and the evolution of the plant variables, can modify the plant identification block, the plant prediction model, and/or the optimizer in order to improve the performance, robustness and operational safety of the overall control system. The real-time comparison results of the designed Expert MPC and a well-designed model predictive controller (MPC) show that the proposed Expert MPC has a significantly better performance and, therefore, higher accuracy and robustness.

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Huang, Zexin, Matthew Best, and James Knowles. "Optimal predictive steering control for autonomous runway exits." Advances in Mechanical Engineering 12, no. 12 (2020): 168781402098086. http://dx.doi.org/10.1177/1687814020980861.

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In this paper, we present a real-time optimal controller, Predictive Steering Control (PSC), to perform high-speed runway exit manoeuvres. PSC is developed based on a time-varying LQR with look-ahead. The aircraft’s ground dynamics are described by a high-fidelity nonlinear model. The proposed controller is compared with an Expert Pilot Model (EPM), which represents a pilot, in several different speed runway exit manoeuvres. With an improved road preview mechanism and optimal feedback gain, the predictive steering controller outperforms the expert pilot’s manual operations by executing the runway exit manoeuvre with a lower track error. To investigate the optimality of PSC, its solution is further optimised using a numerical optimal controller Generalized Optimal Control (GOC). PSC is shown to be close to the final optimal solution. To study robustness, PSC is tested with various aircraft configurations, road conditions and disturbances. The simulation results show that PSC is robust to disturbances within a normal range of operational parameters.

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Im, Eunji, Minji Choi, and Kyunghoon Cho. "Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications." Sensors 24, no. 14 (2024): 4567. http://dx.doi.org/10.3390/s24144567.

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This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the controller to emulate human expert behavior. Rules are represented as Signal Temporal Logic (STL) formulas. A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. In a track driving simulation, our method demonstrates the ability to generate behavior resembling that of human experts and effectively manage rule-based dilemmas.

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Xu, Dongxin, Yueqiang Han, Chang Ge, Longtao Qu, Rui Zhang, and Guoye Wang. "A Model Predictive Control Method for Vehicle Drifting Motions with Measurable Errors." World Electric Vehicle Journal 13, no. 3 (2022): 54. http://dx.doi.org/10.3390/wevj13030054.

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Vehicle drifting control has attracted wide attention, and the study methods are divided into expert-based and theory-based. In this paper, the vehicle drifting control was based on the vehicle drifting state characteristics. The vehicle drifting state parameters were obtained by the theory-based vehicle drifting motion mechanism analysis based on a nonlinear vehicle dynamics model, which was used to express the vehicle characteristics, together with the UniTire model, by considering the vehicle longitudinal, lateral, roll, and yaw motions. A vehicle drifting controller was designed by the model predictive control (MPC) theory and a linear dynamics model with the linearized expressions of nonlinear tire forces based on the consideration of measurable errors. The control targets were the vehicle drifting state parameters obtained by calculation, and the controller performance was proved by simulation in MATLAB/Simulink, demonstrating that the controller is good to realize vehicle drifting motions. The same target drifting motions were realized at different original states, which proved that the vehicle drifting control is possible with the designed controller.

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Jevtovic, Branislav, and Miroslav Matausek. "A predictive-adaptive hierarchical control system of bucket-wheel excavator: Theory and experimental results." Facta universitatis - series: Electronics and Energetics 18, no. 3 (2005): 493–503. http://dx.doi.org/10.2298/fuee0503493j.

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Development of a new two-level hierarchical control system, which significantly increases excavating capacity, as well as availability, and reliability of the bucket wheel excavator, is presented in this paper. On the first ? basic level functions of local regulators and sensors are executed and the second ? higher level is performing adaptation based on prediction of cutting resistance of materials to be excavated. Development of basic control system consists of design and tuning of local regulators, as well as design of highly precise and reliable sensors of basic movements. The predictive?adaptive higher-level control system is a neuro-fuzzy controller. By predicting cutting resistance of materials to be excavated reference of slewing speed and controller parameters are adapted. The structure of the new control system is based on expert knowledge, gained through numerous simulations of developed non-linear model in state space, where the disturbances are precisely modeled, and numerous experiments.

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Cáceres, Gabriela, Pablo Millán, Mario Pereira, and David Lozano. "Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture." Agronomy 11, no. 9 (2021): 1810. http://dx.doi.org/10.3390/agronomy11091810.

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The growth of the global population, together with climate change and water scarcity, has made the shift towards efficient and sustainable agriculture increasingly important. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators offers great opportunities in this direction since these devices can be easily deployed to implement advanced monitoring and irrigation control techniques at a farm scale, saving energy and water and decreasing costs. This paper proposes an economic and periodic predictive controller taking advantage of the irrigation periodicity. The goal of the controller is to find an irrigation technique that optimizes water and energy consumption while ensuring adequate levels of soil moisture for crops, achieving the maximum crop yield. For this purpose, the developed predictive controller makes use of soil moisture data at different depths, and it formulates a constrained optimization problem that considers energy and water costs, crop transpiration, and an accurate dynamical nonlinear model of the water dynamics in the soil, reflecting the reality. This controller strategy is compared with a classical irrigation strategy adopted by a human expert in a specific case study, demonstrating that it is possible to obtain significant reductions in water and energy consumption without compromising crop yields.

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Lee, Taekgyu, Dongyoon Seo, Jinyoung Lee, and Yeonsik Kang. "Real-Time Drift-Driving Control for an Autonomous Vehicle: Learning from Nonlinear Model Predictive Control via a Deep Neural Network." Electronics 11, no. 17 (2022): 2651. http://dx.doi.org/10.3390/electronics11172651.

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A drift-driving maneuver is a control technique used by an expert driver to control a vehicle along a sharply curved path or slippery road. This study develops a nonlinear model predictive control (NMPC) method for the autonomous vehicle to perform a drift maneuver and generate the datasets necessary for training the deep neural network(DNN)-based drift controller. In general, the NMPC method is based on numerical optimization which is difficult to run in real-time. By replacing the previously designed NMPC method with the proposed DNN-based controller, we avoid the need for complex numerical optimization of the vehicle control, thereby reducing the computational load. The performance of the developed data-driven drift controller is verified through realistic simulations that included drift scenarios. Based on the results of the simulations, the DNN-based controller showed similar tracking performance to the original nonlinear model predictive controller; moreover, the DNN-based controller can demonstrate stable computation time, which is very important for the safety critical control objective such as drift maneuver.

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Safari, Ashkan, Hossein Hassanzadeh Yaghini, Hamed Kharrati, Afshin Rahimi, and Arman Oshnoei. "Voltage Controller Design for Offshore Wind Turbines: A Machine Learning-Based Fractional-Order Model Predictive Method." Fractal and Fractional 8, no. 8 (2024): 463. http://dx.doi.org/10.3390/fractalfract8080463.

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Integrating renewable energy sources (RESs), such as offshore wind turbines (OWTs), into the power grid demands advanced control strategies to enhance efficiency and stability. Consequently, a Deep Fractional-order Wind turbine eXpert control system (DeepFWX) model is developed, representing a hybrid proportional/integral (PI) fractional-order (FO) model predictive random forest alternating current (AC) bus voltage controller designed explicitly for OWTs. DeepFWX aims to address the challenges associated with offshore wind energy systems, focusing on achieving the smooth tracking and state estimation of the AC bus voltage. Extensive comparative analyses were performed against other state-of-the-art intelligent models to assess the effectiveness of DeepFWX. Key performance indicators (KPIs) such as MAE, MAPE, RMSE, RMSPE, and R2 were considered. Superior performance across all the evaluated metrics was demonstrated by DeepFWX, as it achieved MAE of [15.03, 0.58], MAPE of [0.09, 0.14], RMSE of [70.39, 5.64], RMSPE of [0.34, 0.85], as well as the R2 of [0.99, 0.99] for the systems states [X1, X2]. The proposed hybrid approach anticipates the capabilities of FO modeling, predictive control, and random forest intelligent algorithms to achieve the precise control of AC bus voltage, thereby enhancing the overall stability and performance of OWTs in the evolving sector of renewable energy integration.

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Ugwuanyi, Hyginus Sunday, and Joseph Udokamma Ugwuanyi. "Smart Control Solution for Single-Stage Solar PV Systems." European Journal of Electrical Engineering and Computer Science 7, no. 6 (2023): 38–45. http://dx.doi.org/10.24018/ejece.2023.7.6.582.

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Solar photovoltaic (PV) systems unpredictable characteristics and tight grid-codes demand power electronic-based energy conversion devices. Hence, as the power levels generated by the solar PV systems rise, multi-level voltage source converters (VSC) and their control mechanisms become more necessary for effective energy conversion. Continuous control set model predictive control (CCS-MPC) is a class of predictive control approach that has emerged recently for the applications of power converters and energy conversion systems. In this paper, an artificial neural network (ANN) based controller for single-stage grid-connected PV is implemented. The CCS-MPC is used as an expert / a teacher to generate the data required for off-line training of the neural network controller. After the off-line training, the trained ANN can fully control the inverter’s output voltage and track the maximum power point (MPP) without the need for MPC during testing. The proposed control technique is assessed under various operating conditions. Based on the results obtained, it is observed that the proposed techniques offer improved objective tracking and comparative dynamic response with respect to the classical approaches.

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Elsisi, Mahmoud, Minh-Quang Tran, Hany M. Hasanien, Rania A. Turky, Fahad Albalawi, and Sherif S. M. Ghoneim. "Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms." Mathematics 9, no. 22 (2021): 2885. http://dx.doi.org/10.3390/math9222885.

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This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

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Zhao, Liang, Wen Tao, Guangwen Wang, Lida Wang, and Guichang Liu. "Intelligent anti-corrosion expert system based on big data analysis." Anti-Corrosion Methods and Materials 68, no. 1 (2021): 17–28. http://dx.doi.org/10.1108/acmm-10-2020-2384.

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Purpose The paper aims to develop an intelligent anti-corrosion expert system based on browser/server (B/S) architecture to realize an intelligent corrosion management system. Design/methodology/approach The system is based on Java EE technology platform and model view controller (MVC) three-tier architecture development model. The authors used an extended three-dimensional interpolation model to predict corrosion rate, and the model is verified by cross-validation method. Additionally, MySQL is used to realize comprehensive data management. Findings The proposed anti-corrosion system thoroughly considers a full use of corrosion data, relevant corrosion prediction and efficient corrosion management in one system. Therefore, this system can achieve an accurate prediction of corrosion rate, risk evaluation, risk alert and expert suggestion for equipment in petrochemical plants. Originality/value Collectively, this present study has important ramifications for the more efficient and scientific management of corrosion data in enterprises and experts’ guidance in controlling corrosion status. At the same time, the digital management of corrosion data can provide a data support for related theoretical researches in corrosion field, and the intelligent system also offers examples in other fields to improve system by adding intelligence means.

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Ducard, Guillaume, and Gregorio Carughi. "Neural Network Design and Training for Longitudinal Flight Control of a Tilt-Rotor Hybrid Vertical Takeoff and Landing Unmanned Aerial Vehicle." Drones 8, no. 12 (2024): 727. https://doi.org/10.3390/drones8120727.

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This paper considers a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). By tilting its propellers, the aircraft can transition from rotary-wing (RW) multirotor mode to fixed-wing (FW) mode and vice versa. A novel architecture of a neural network-based controller (NNC) is presented. An “imitative learning” approach is employed to train the NNC to mimic the response of an expert but computationally expensive model predictive controller (MPC). The resulting NNC approximates the MPC’s solution while significantly decreasing the computational cost. The NNC is trained on the longitudinal axis. Successful simulations and real flight tests prove that the NNC is suitable for the longitudinal axis control of a complex nonlinear system such as the tilt-rotor VTOL UAV through a sequence of transitions between the RW mode to the FW mode, and vice versa, in a forward flight.

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Youn, Ji-Hee, Joanne Lord, Karla Hemming, Alan Girling, and Martin Buxton. "BAYESIAN META-ANALYSIS ON MEDICAL DEVICES: APPLICATION TO IMPLANTABLE CARDIOVERTER DEFIBRILLATORS." International Journal of Technology Assessment in Health Care 28, no. 2 (2012): 115–24. http://dx.doi.org/10.1017/s0266462312000037.

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Objectives: The aim of this study is to describe and illustrate a method to obtain early estimates of the effectiveness of a new version of a medical device.Methods: In the absence of empirical data, expert opinion may be elicited on the expected difference between the conventional and modified devices. Bayesian Mixed Treatment Comparison (MTC) meta-analysis can then be used to combine this expert opinion with existing trial data on earlier versions of the device. We illustrate this approach for a new four-pole implantable cardioverter defibrillator (ICD) compared with conventional ICDs, Class III anti-arrhythmic drugs, and conventional drug therapy for the prevention of sudden cardiac death in high risk patients. Existing RCTs were identified from a published systematic review, and we elicited opinion on the difference between four-pole and conventional ICDs from experts recruited at a cardiology conference.Results: Twelve randomized controlled trials were identified. Seven experts provided valid probability distributions for the new ICDs compared with current devices. The MTC model resulted in estimated relative risks of mortality of 0.74 (0.60–0.89) (predictive relative risk [RR] = 0.77 [0.41–1.26]) and 0.83 (0.70–0.97) (predictive RR = 0.84 [0.55–1.22]) with the new ICD therapy compared to Class III anti-arrhythmic drug therapy and conventional drug therapy, respectively. These results showed negligible differences from the preliminary results for the existing ICDs.Conclusions: The proposed method incorporating expert opinion to adjust for a modification made to an existing device may play a useful role in assisting decision makers to make early informed judgments on the effectiveness of frequently modified healthcare technologies.

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Sagdatullin, A. M. "Investigation of the Possibility of Building a Neural Fuzzy Logic Controller with Discrete Terms for Controlling and Automating Oil and Gas Engineering Facilities." Intellekt. Sist. Proizv. 19, no. 3 (2021): 105–10. http://dx.doi.org/10.22213/2410-9304-2021-3-105-110.

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The issue of increasing the efficiency of functioning of classical control systems for technological processes and objects of oil and gas engineering is investigated. The relevance of this topic lies in the need to improve the quality of the control systems for the production and transportation of oil and gas. The purpose of the scientific work is to develop a neuro-fuzzy logic controller with discrete terms for the control and automation of pumping units and pumping stations. It is noted that fuzzy logic, neural network algorithms, together with control methods based on adaptation and synthesis of control objects, make it possible to learn the automation system and work under conditions of uncertainty. Methods for constructing classical control systems are studied, the advantages and disadvantages of fuzzy controllers, as the main control system, are analyzed. A method for constructing a control system based on a neuro-fuzzy controller with discrete terms in conditions of uncertainty and dynamic parameters of the process is proposed. The positive features of the proposed regulator include a combination of fuzzy reasoning about a technological object and mathematical predictive models, a fuzzy control system gains the possibility of subjective description based on neural network structures, as well as adaptation to the characteristics of the object. The graph of dependence for the term-set of the controlled parameter on the degree of membership is presented. A possible implementation of tracking the triggering of one of the rules of the neuro-fuzzy system in the format of functional block diagrams is presented. The process of forming an expert knowledge base in a neuro-fuzzy control system is considered. For analysis, a graph of the dependence of the output parameter values is shown. According to the results obtained, the deviation of the values for the model and the real process does not exceed 18%, which allows us to speak of a fairly stable operation of the neuro-fuzzy controller in automatic control systems.

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Bhardwaj, Suraj. "A Critical Analysis of The Application of Artificial Neural Network (ANN) in the Field of DC to DC Converters." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem41916.

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Because of the fast advancement of renewable energy technologies, the idea of microgrid (MG) is becoming more commonly adopted in power systems. DC MG is gaining popularity due to the benefits of the DC distribution system, including as easier integration of energy storage and lower system loss. The linear controller, such as PI or PID, is established and widely used in the power electronics sector, but its performance degrades as system parameters change. In this paper, an artificial neural network (ANN)-based voltage control technique for a DC- DC boost converter is developed. The model predictive control (MPC) is employed as an expert in this study, providing data to train the suggested ANN. Because ANN is highly adjusted, it is used directly to regulate the step-up DC converter. The key benefit of the ANN is that it reduces the inaccuracy of the system model even with erroneous parameters and has a lower computing overhead than MPC owing to its parallel nature. Extensive MATLAB/Simulink simulations are run to validate the performance of the proposed ANN. The simulation findings reveal that the ANN-based control method outperforms the PI controller under various loading situations. The trained ANN model has an accuracy of roughly 97%, making it acceptable for DC microgrid applications.. Index Terms—ANN, DC Microgrid, DC/DC boost converter, MPC, Primary control.

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Sadeghi, Maryam, and Majid Gholami. "IEC 61499 in Distributed Control of Weather Short-Term Load Forecasting Using Fuzzy Logic Algorithm." Advanced Materials Research 433-440 (January 2012): 3929–33. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3929.

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Distributed Control System (DCS) equipping the new design methodology comprises an open architecture for intelligent and agile control of distributed control systems by developing a novel international standard “IEC 61499” evolving the event driven functional modules distributed to field devices and interconnected among multiple controllers. It is investigated for predicting the short term power demand using weather and ambient conditions such as temperature, humidity, season, wind and precipitation. Forecasting algorithm simulated via Function Block Development Kit (FBDK) using Fuzzy Logic Controller (FLC). FLC is an advanced method for prediction and control of nonlinear system which is based on fuzzy logic concept comprising an algorithms formulated by linguistically expert rules. Precise mathematical model free system, robustness and flexibility in the event of parameter variations are the most advantages of FLC. In this approach three distributed weather stations are defined for estimating the power demand in a small area using IEC 61499 DCS standard and FLC as a prediction logic. IEC 61499 intensifies flexibility by capability in adaption and system reconfiguration in regard of environment changes, results on cost reduction and diminutions the industrial automation complexity. It increasingly enlarges the adaptability of proposed control system, enhances the system portability, interoperability and develops configurability. IEC 61499 facilitates world trade by swooping technical barriers to trade, eventuates the neoteric markets and economic growth and leads to a strong trend towards distributed automation systems.

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Qiao, Yiran, Xinbo Chen, and Dongxiao Yin. "Coordinated Control for the Trajectory Tracking of Four-Wheel Independent Drive–Four-Wheel Independent Steering Electric Vehicles Based on the Extension Dynamic Stability Domain." Actuators 13, no. 2 (2024): 77. http://dx.doi.org/10.3390/act13020077.

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In order to achieve multi-objective chassis coordination control for 4WID-4WIS (four-wheel independent drive–four-wheel independent steering) electric vehicles, this paper proposes a coordinated control strategy based on the extension dynamic stability domain. The strategy aims to improve trajectory tracking performance, handling stability, and economy. Firstly, expert PID and model predictive control (MPC) are used to achieve longitudinal speed tracking and lateral path tracking, respectively. Then, a sliding mode controller is designed to calculate the expected yaw moment based on the desired vehicle states. The extension theory is applied to construct the extension dynamic stability domain, taking into account the linear response characteristics of the vehicle. Different coordinated allocation strategies are devised within various extension domains, providing control targets for direct yaw moment control (DYC) and active rear steering (ARS). Additionally, a compound torque distribution strategy is formulated to optimize driving efficiency and tire adhesion rate, considering the vehicle’s economy and stability requirements. The optimal wheel torque is calculated based on this strategy. Simulation tests using the CarSim/Simulink co-simulation platform are conducted under slalom test and double-lane change to validate the control strategy. The test results demonstrate that the proposed control strategy not only achieves good trajectory tracking performance but also enhances handling stability and economy during driving.

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Zhou, Yi. "A Summary of PID Control Algorithms Based on AI-Enabled Embedded Systems." Security and Communication Networks 2022 (April 23, 2022): 1–7. http://dx.doi.org/10.1155/2022/7156713.

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Proportional-integral-derivative (PID) controllers are extensively used in engineering practices for their simple structures, robustness to model errors, and easy operations. At present, there is a great variety of PID controllers. Companies have developed intelligent regulators with functions for automatically tuning PID parameters. For present PID controllers, strategies such as intelligence, self-adaptation, and self-correction are extended to transmission PID. PID controllers and corresponding improved ones are utilized in 90% of industrial control processes. In this paper, PID control algorithms are summarized. This paper focuses on advanced control strategies such as PID control, predictive PID control, adaptive PID control, fuzzy PID control, neural network PID control, expert intelligent PID control, PID control based on genetic algorithms, and PID control based on ant colony algorithms. Besides, these kinds of algorithms are compared, and prospects of PID algorithms are forecast at the end of this paper.

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Oghorodi, D., E. J. Atajeromavwo, A. E. Okpako, et al. "A Cutting-Edge Approach to Predictive Precision in Oncology Using a Geneto-Neuro-Fuzzy Hybrid Model." AFRICAN JOURNAL OF APPLIED RESEARCH 11, no. 1 (2025): 766–85. https://doi.org/10.26437/ajar.v11i1.880.

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Purpose: This study introduces a pioneering hybrid model that combines genetic algorithms, neuro-fuzzy logic, and mobile agent technology to enhance predictive precision for early-stage prostate cancer diagnosis. Design/Methodology/Approach: One hundred and twenty records of prostate cancer patients were initially collected from the Delta State University Teaching Hospital, Oghara, Nigeria. Each patient’s record included relevant data on prostate disease, such as age, PSA levels, clinical history, symptom severity, biopsy results, and other demographic and clinical factors. This data was extracted and stored as rules in a MySQL database, with the MySQL Fuzzy Extension enabling fuzzy data storage and processing. Findings: Extensive simulations and clinical data analyses demonstrate the model’s superior sensitivity and specificity in detecting early-stage prostate cancer compared to traditional diagnostic methods. Medical expert evaluations validate the model’s effectiveness as a promising diagnostic alternative. Research Limitation: While results are promising, the study is limited to simulations and a controlled clinical dataset. Practical Implications: The system offers a practical, scalable early prostate cancer detection solution that could revolutionise current diagnostic practices. Social Implications: Potential social benefits include improved patient outcomes, reduced healthcare costs, and better quality of life. Originality/Value: This study presents an innovative integration of genetic algorithms, neuro-fuzzy systems, and mobile agent technology. This novel approach paves the way for advanced cancer diagnostics and precision medicine.

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Jimola, Folasade Esther. "Teaching with Styles: A Predictive Factor for Improved Students’ Learning Outcomes in Classrooms." Journal of Elementary and Secondary School 2, no. 1 (2024): 47–58. http://dx.doi.org/10.31098/jess.v2i1.2118.

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The manner in which a teacher teaches and relates with students is influenced and controlled by some teacher-related factors such as the style of teaching. This study examines (i) the teaching styles adopted by Literature-in-English teachers, (ii) Literature-in-English students’ perception of and preference for teachers’ teaching styles, and (iii) whether teachers’ teaching styles predict students’ achievement and attitude toward Literature-in-English. The study was a descriptive research of the survey type that employed four research instruments. The study consisted of 759 respondents (127 Literature-in-English teachers and 632 public senior secondary school II Literature-in-English students) in Ekiti State, Nigeria. The data collected for this study were analyzed using descriptive and inferential statistics. It was evident in the research that Literature-in-English teachers employed delegator, facilitator, and role model teaching styles while expert and formal authority styles were the least. The findings revealed that teacher teaching style is a good a predictor of students’ achievement and attitude toward Literature-in-English. Based on these findings, profound pedagogical implications and relevant recommendations were made for concerned education stakeholders.

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Igbinake, Augustine. "Estimation and Optimization of Specific Heat of TIG Weld of Mild Steel (s275) Using Response Surface Methodology." Applied Engineering 9, no. 1 (2025): 37–44. https://doi.org/10.11648/j.ae.20250901.13.

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Specific heat, an intrinsic thermal property, represents the amount of heat energy required to raise the temperature of a substance by one degree Celsius. Accurate estimation of specific heat in welded metals is crucial for understanding thermal behavior during and after welding processes, especially in applications where temperature control and energy efficiency are essential. This study focuses on the prediction and optimization of the specific heat of mild steel weldments using Response Surface Methodology (RSM), a statistical technique for modeling and analyzing the effects of multiple variables. A total of 100 welded mild steel specimens, each measuring 60 mm × 40 mm × 10 mm, were prepared through controlled Tungsten Inert Gas (TIG) welding operations. During the experiments, key process parameters - welding current, arc voltage, and shielding gas flow rate - were systematically varied to observe their effect on specific heat. The experimental data collected were analyzed using Design Expert 13 software, enabling statistical modeling, regression analysis, and optimization. A second-order quadratic model was developed to describe the relationship between specific heat and the input parameters. The optimal parameter combination was determined to be 180 A, 19 V, and 13 L/min, resulting in a predicted specific heat value of 445.106 J/kg°C. The developed model provides a useful predictive tool for future thermal analysis of welded structures.

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Lv, Yukang, Yi Chen, Ziguo Chen, et al. "Hybrid Supervised and Reinforcement Learning for Motion-Sickness-Aware Path Tracking in Autonomous Vehicles." Sensors 25, no. 12 (2025): 3695. https://doi.org/10.3390/s25123695.

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Path tracking is an essential task for autonomous driving (AD), for which controllers are designed to issue commands so that vehicles will follow the path of upper-level decision planning properly to ensure operational safety, comfort, and efficiency. Current path-tracking methods still face challenges in balancing tracking accuracy with computational overhead, and more critically, lack consideration for Motion Sickness (MS) mitigation. However, as AD applications divert occupants’ attention to non-driving activities at varying degrees, MS in self-driving vehicles has been significantly exacerbated. This study presents a novel framework, the Hybrid Supervised–Reinforcement Learning (HSRL), designed to reduce passenger discomfort while achieving high-precision tracking performance with computational efficiency. The proposed HSRL employs expert data-guided supervised learning to rapidly optimize the path-tracking model, effectively mitigating the sample efficiency bottleneck inherent in pure Reinforcement Learning (RL). Simultaneously, the RL architecture integrates a passenger MS mechanism into a multi-objective reward function. This design enhances model robustness and control performance, achieving both high-precision tracking and passenger comfort optimization. Simulation experiments demonstrate that the HSRL significantly outperforms Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC), achieving improved tracking accuracy and significantly reducing passengers’ cumulative Motion Sickness Dose Value (MSDV) across several test scenarios.

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Chai, Tianyou, Fenghua Wu, Jinliang Ding, and Chun-Yi Su. "Intelligent work-situation fault diagnosis and fault-tolerant system for the shaft-furnace roasting process." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 6 (2007): 843–55. http://dx.doi.org/10.1243/09596518jsce364.

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During roasting in a shaft furnace (used for the deoxidizing roasting of ore), work-situation faults (WSFs) arise as a result of variations in process conditions and off-spec operation. These work-situation faults can be potentially disastrous and can lead to a total collapse of the control system if they are not detected and diagnosed in time. Furthermore, by their very nature they have to be distinguished from the results addressed by existing methods of diagnosis and tolerance control. This paper presents an innovative work-situation fault diagnosis (WSFD) and fault-tolerance control (FTC) strategy for a control system where a combination of neural networks, expert system, and case-based reasoning is used. As such, a system is established that consists of a magnetic tube recovery rate (MTRR) prediction model, a work-situation fault diagnosis unit, and a fault-tolerance controller. The proposed system diagnoses imminent work-situation faults, and then the fault-tolerance controller adjusts the set-points of the control loops. The outputs of the lower-level control system track the modified set-points, which makes the process deviate gradually from work-situation faults with an acceptable product quality. The proposed system has been applied to the shaft-furnace roasting process in the largest minerals processing factory in China and has reduced the frequency of all work-situation faults by more than 50 per cent, with the ratio of furnace operation increased by 2.98 per cent. It has been proven to provide many benefits to the factory.

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Yao, Kai-Chao, Li-Chiou Hsu, Jiunn-Shiou Fang, Yi-Jung Chen, and Zhou-Kai Guo. "The Establishment and Evaluation Model of the Thematic Deep-Learning Teaching Module." Applied Sciences 15, no. 5 (2025): 2335. https://doi.org/10.3390/app15052335.

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In recent years, the application of artificial intelligence (AI) in industry has matured, requiring deeper learning and integration of existing technologies. This study started with technical education to improve the professional quality of human resources. The double-triangular fuzzy number and gray area testing methods in the fuzzy Delphi method (FDM) were used to evaluate expert consensus, plan technical capability indicators, and ensure the integrity and appropriateness of teaching materials. Based on these indicators, special subject teaching course units were designed and integrated into existing courses for experimental teaching and evaluation. The teaching module arrangement in this research used a virtual instrument control system with LabVIEW v2021 as the GUI and the myRIO controller. The proposed system integrates an artificial neural network (ANN) AI model built with Python v3.7 for data analysis and prediction, forming an embedded teaching module for a deep learning-oriented intelligent robotic environmental monitoring system. This study evaluated students’ acceptance of deep learning robotics teaching modules and their impact on improving their technical skills. The psychomotor scale established by the scholars was adopted and revised, including this study’s technical ability indicators. The test-retest reliability of the psychomotor scale was high. The results revealed that the post-test scores of the psychomotor scale were significantly better than those of the pre-test, indicating that students’ overall technical abilities improved. Students’ affective attitudes toward the four dimensions of teaching material and equipment, cognitive development, skills performance, and self-exploration were positive. Feedback revealed that students who participated in the teaching experiment responded positively on all levels of the affective scale, indicating increased motivation and willingness to continue learning. This study successfully constructed a teaching module and evaluation model for deep learning robotic environmental sensing and control. The teaching module and evaluation model established through this research contribute to the cultivation and effectiveness evaluation of relevant technical talents.

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Ramakrishnam Raju, S. V. S., Bhasker Dappuri, P. Ravi Kiran Varma, Murali Yachamaneni, D. Marlene Grace Verghese, and Manoj Kumar Mishra. "Design and Implementation of Smart Hydroponics Farming Using IoT-Based AI Controller with Mobile Application System." Journal of Nanomaterials 2022 (July 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/4435591.

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Hydroponics is the soil less agriculture farming, which consumes less water and other resources as compared to the traditional soil-based agriculture systems. However, monitoring of hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters, nutrition suggestion, and plant diagnosis system. But the recent technological developments are quite useful to solve these problems by adopting the artificial intelligence-based controlling algorithms in agriculture sector. Therefore, this article focuses on implementation of mobile application integrated artificial intelligence based smart hydroponics expert system, hereafter referred as AI-SHES with Internet of Things (IoT) environment. The proposed AI-SHES with IoT consists of three phases, where the first phase implements hardware environment equipped with real-time sensors such as NPK soil, sunlight, turbidity, pH, temperature, water level, and camera module which are controlled by Raspberry Pi processor. The second phase implements deep learning convolutional neural network (DLCNN) model for best nutrient level prediction and plant disease detection and classification. In third phase, farmers can monitor the sensor data and plant leaf disease status using an Android-based mobile application, which is connected over IoT environment. In this manner, the farmer can continuously track the status of his field using the mobile app. In addition, the proposed AI-SHES also develops the automated mode, which makes the complete environment in automatic control manner and takes the necessary actions in hydroponics field to increase the productivity. The obtained simulation results on disease detection and classification using proposed AI-SHES with IoT disclose superior performance in terms of accuracy, F-measure with 99.29%, and 99.23%, respectively.

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Tang, Shanrong, Ke Zhu, and Peiwen Guo. "Research on Quantitative Assessment and Dynamic Reasoning Method for Emergency Response Capability in Prefabricated Construction Safety." Buildings 13, no. 9 (2023): 2311. http://dx.doi.org/10.3390/buildings13092311.

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In response to the common issues of lacking a comprehensive quantitative assessment system and insufficient dynamic understanding of emergency response capability in prefabricated construction safety, this study proposes a research methodology based on decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps (FCM) to promote the construction of emergency response capacity. Firstly, a quantitative evaluation indicator system comprising 4 core categories of organizational management, personnel quality, technical measures, and emergency resources, along with 16 main categories, is established using grounded theory and three levels of coding approach. Subsequently, through a combination of expert surveys and quantitative analysis, DEMATEL is employed to unveil the causal relationships and key indicators of the evaluation criteria. Next, the DEMATEL and FCM models are integrated to conduct predictive and diagnostic reasoning analysis based on key indicators. Finally, a case study is conducted to validate the usability and effectiveness of the proposed model and methodology. The results demonstrate that indicators related to organizational management and personnel quality belong to the cause group, while technical measures and emergency resources fall into the effect group. The “completeness of emergency plans” exhibits the most significant influence on other indicators and is also the most influenced indicator by others. Predictive reasoning analysis reveals that well-controlled “emergency organizational structure and procedures” are crucial for enhancing emergency response capacity. Diagnostic reasoning analysis indicates that the improvement of emergency response capability should focus on enhancing the “completeness of emergency plans”. The synergistic effect between “emergency organizational structure and procedures” and “completeness of emergency plans” contributes to the enhancement of emergency response capability in prefabricated construction safety. The study holds both theoretical and practical significance for advancing safety management in prefabricated construction. Considering the dynamic coupling of multiple factors will be the primary direction of research in the field of safety management in the future.

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Vozák, Daniel, and Vojtech Veselý. "Robust Model Predictive Controller Design." IFAC Proceedings Volumes 47, no. 3 (2014): 7443–48. http://dx.doi.org/10.3182/20140824-6-za-1003.01346.

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Rezaee, Alireza. "Model predictive Controller for Mobile Robot." Transactions on Environment and Electrical Engineering 2, no. 2 (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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Ripy, John, Ted Grossardt, Michael Shouse, Philip Mink, Keiron Bailey, and Carl Shields. "Expert Systems Archeological Predictive Model." Transportation Research Record: Journal of the Transportation Research Board 2403, no. 1 (2014): 37–44. http://dx.doi.org/10.3141/2403-05.

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Bai, Guoxing, Li Liu, Yu Meng, Weidong Luo, Qing Gu, and Baoquan Ma. "Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control." Applied Sciences 9, no. 7 (2019): 1372. http://dx.doi.org/10.3390/app9071372.

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Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.

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Hamane, Hiroto, Go Fujikawa, Yoichi Hayashi, and Kazuyoshi Miyazaki. "Model Predictive Control for Programmable Controller." IEEJ Transactions on Industry Applications 127, no. 12 (2007): 1245–46. http://dx.doi.org/10.1541/ieejias.127.1245.

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Heirung, Tor Aksel N., B. Erik Ydstie, and Bjarne Foss. "An Adaptive Model Predictive Dual Controller." IFAC Proceedings Volumes 46, no. 11 (2013): 62–67. http://dx.doi.org/10.3182/20130703-3-fr-4038.00098.

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Schlegel, Miloš, and Jaroslav Sobota. "Simple Pulse-Step Model Predictive Controller." IFAC Proceedings Volumes 41, no. 2 (2008): 8401–6. http://dx.doi.org/10.3182/20080706-5-kr-1001.01420.

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Li, Pu, Moritz Wendt, and Günter Wozny. "A probabilistically constrained model predictive controller." Automatica 38, no. 7 (2002): 1171–76. http://dx.doi.org/10.1016/s0005-1098(02)00002-x.

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Åkesson, Bernt M., and Hannu T. Toivonen. "A neural network model predictive controller." Journal of Process Control 16, no. 9 (2006): 937–46. http://dx.doi.org/10.1016/j.jprocont.2006.06.001.

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Van Brempt, Wim, Ton Backx, Jobert Ludlage, Peter Van Overschee, Bart De Moor, and R. Tousain. "A high performance model predictive controller:." Control Engineering Practice 9, no. 8 (2001): 829–35. http://dx.doi.org/10.1016/s0967-0661(01)00047-8.

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Scattolini, R., and N. Schiavoni. "A multirate model-based predictive controller." IEEE Transactions on Automatic Control 40, no. 6 (1995): 1093–97. http://dx.doi.org/10.1109/9.388691.

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Moro, Tony L., and Nunzio Bonavita. "IDCOM - B multivariable model-predictive controller." Transactions of the Institute of Measurement and Control 19, no. 4 (1997): 192–201. http://dx.doi.org/10.1177/014233129701900404.

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Fruzzetti, Keith P., Ahmet Palazoglu, Jose A. Romagnoli, and Karen A. McDonald. "A robust/cautious model predictive controller." Journal of Process Control 1, no. 4 (1991): 187–96. http://dx.doi.org/10.1016/0959-1524(91)85008-7.

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Guerlain, Stephanie, Greg Jamieson, and Peter Bullemer. "Visualizing Model-Based Predictive Controllers." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 22 (2000): 511–14. http://dx.doi.org/10.1177/154193120004402203.

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One common problem with information displays, particularly in process control, is that relevant data is often scattered across several, separate displays that obscure important relationships and fail to show event information. The current displays used for model-based predictive controllers demonstrate several of the problems that this kind of a design can incur. It is hard to get a good sense of the recent, current and near-future status of the controller (situation awareness), and it is difficult to make informed decision when making changes to the controller (putting data into context). This forces users to navigate around a virtual workspace and attempt to compile the data necessary to make an informed conclusion. We have applied several design principles to show how it is possible to re-represent data into hierarchical data layers that support the cognitive tasks of monitoring, diagnosis, and control. This design forms a coherent, coordinated workspace which helps orient users to problems in the controller, with direct navigation to supporting details.

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Sheh Zad, Haris, Abasin Ulasyar, Adil Zohaib, and Abraiz Khattak. "Adaptive sliding mode predictive power control of three-phase AC/DC converters." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 236, no. 5 (2022): 897–912. http://dx.doi.org/10.1177/09596518221079469.

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This article presents a new adaptive sliding mode–based model predictive controller for AC/DC three-phase converters achieving better dynamic performance and stability. In the classical model predictive controller available in the literature, the model-based approach, for example, proportional–integral controller is employed for producing the active power reference for the three-phase converters. The traditional proportional–integral–based model predictive controllers consist of steady-state error and slow transient response characteristics. As a result, the DC-link voltage contains uncertainties due to variations in the load demand and output voltage. To overcome these limitations, this article suggests an adaptive sliding mode controller for generating the active power reference value from the DC-link voltage which then combines with the model predictive controller in order to minimize the cost function. The proposed controller minimizes the effects of uncertainties and variations in the output voltage by adaptively regulating the gain of sliding mode controller and modifying the control law online. The cost function is then minimized using the model predictive controller in order to control the active and reactive power flow. The stability analysis of the designed controller is performed using Lyapunov theorem. The effectiveness of the designed control scheme is proved by comparing its performance with the proportional–integral model predictive controller and fixed gain sliding mode–based model predictive controller control schemes. Simulation and experimental system results are obtained for validating the presented control approach.

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Srikshana, Sasidaran, R. Adithya, Raja V. Haris, and M.P.Anbarasi. "Recent Trends in Model Predictive Control." International Journal of Innovative Science and Research Technology 7, no. 2 (2022): 249–54. https://doi.org/10.5281/zenodo.6323081.

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In this paper we are going to present the recent trends of model predictive control (MPC) and its techniques are used in modern world. MPC forecasts plant output behavior using a plant model. The MPC controller solves the optimization problem across the prediction horizon while adhering to the constraints at the current phase. This can be used in non-linear problems and it is more precise when compare to the linear controller such as PID.

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Andrei, Andrei Maxim, and Costin Sorin Bildea. "Linear Model Predictive Control of Olefin Metathesis Process." Processes 11, no. 7 (2023): 2216. http://dx.doi.org/10.3390/pr11072216.

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The applicability of linear model predictive control to the 2-butene metathesis process is studied. Similarly to industrial practice, the model predictive controller is configured on a supervisory level, providing set points to basic process controllers. The development of the process model is based on open-loop identification from input–output data extracted from dynamic simulation performed in Aspen Plus Dynamics. The model predictive controller, designed using MATLAB tools, supervises a system consisting of two inputs (feed rate and reaction temperature) and two outputs (ethylene and propylene production rates). The performance of the model-based control strategy is assessed by Aspen Plus Dynamics-Simulink co-simulation and compared to regulatory control through several indexes (mean square error, integral square error, peak error, and integral absolute error). The model predictive controller outperforms the feedback controller. Considerations regarding the workflow for the implementation of model predictive control in an industrial environment are provided.

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Khac, Hoang Nguyen, Amin Modabberian, Xiaoguo Storm, Kai Zenger, and Jari Hyvönen. "Model predictive control for a multiple injection combustion model." Open Engineering 11, no. 1 (2021): 1134–40. http://dx.doi.org/10.1515/eng-2021-0113.

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Abstract In this work, a model predictive controller is developed for a multiple injection combustion model. A 1D engine model with three distinct injections is used to generate data for identifying the state-space representation of the engine model. This state-space model is then used to design a controller for controlling the start of injection and injected fuel mass of the post injection. These parameters are used as inputs for the engine model to control the maximum cylinder pressure and indicated mean effective pressure.

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R. Karpe, Suraj, S. A. Deokar, and U. B. Shinde. "Electrical Drives Using Model Predictive Controller Strategies." Journal of Control and Instrumentation Engineering 9, no. 1 (2023): 31–42. http://dx.doi.org/10.46610/jcie.2023.v09i01.004.

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High-level control techniques in power gadgets incorporate Predictive controller of current (PC CONTROL) and Predictive controller of torque (PT CONTROL). It is simple to include system restrictions. There is no need for the weighting component. Together with the PT CONTROL and PC controller systems, the SRM method is the most practicable direct control technique since it doesn't require a modulator and offers 10% to 30% more power than an induction motor. With the same current, an induction motor can only generate between 70 and 90 per cent of the force generated by an SRM due to its lagging power factor. When compared to the PT CONTROL and PC controller method employing an induction motor and a 15-level H-bridge multilevel inverter, the SRM approach reduces THD in torque, speed, and stator current by 23 per cent. The transistors are only swapped when necessary to maintain the limits of flux and torque, which minimizes switching losses. To improve the efficiency of a multilevel inverter, semiconductor switches are switched in a specific pattern. In contrast to the PT CONTROL and PC controller approaches using a 2-level voltage source inverter, the fifteen-level H-bridge multilevel inverter employed in this study, coupled with SRM and IM, gives outstanding torque and flux responses and achieves stable and robust operation. This unique strategy quickly caught the interest of academics due to its simple method.

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Saleh, Hiba, and Thakwan Salim. "Design and Implementation of Model Predictive Controller." Al-Rafidain Engineering Journal (AREJ) 27, no. 1 (2022): 213–24. http://dx.doi.org/10.33899/rengj.2022.130477.1108.

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Chrif, Labane, and Zemalache Meguenni Kadda. "Aircraft Control System Using Model Predictive Controller." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 259. http://dx.doi.org/10.11591/tijee.v15i2.1538.

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This paper concerns the application of model-based predictive control to the longitudinal and lateral mode of an aircraft in a terrain following task. The predictive control approach was based on a quadratic cost function and a linear state space prediction model with input and state constraints. The optimal control was obtained as the solution of a quadratic programming problem defined over a receding horizon. Closed-loop simulations were carried out by using the linear aircraft model. This project thesis provides a brief overview of Model Predictive Control (MPC).A brief history of industrial model predictive control technology has been presented first followed by a some concepts like the receding horizon, moves etc. which form the basis of the MPC. It follows the Optimization problem which ultimately leads to the description of the Dynamic Matrix Control (DMC).The MPC presented in this report is based on DMC. After this the application summary and the limitations of the existing technology has been discussed and the next generation MPC, with an emphasis on potential business and research opportunities has been reviewed. Finally in the last part we generate Matlab code to implement basic model predictive controller and introduce noise into the model.

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Ewald, Grzegorz, and Mietek A. Brdys. "Model Predictive Controller for Networked Control Systems." IFAC Proceedings Volumes 43, no. 8 (2010): 274–79. http://dx.doi.org/10.3182/20100712-3-fr-2020.00046.

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Hachimi, Mohamed El, Abdelhakim Ballouk, Ilyass Khelafa, and Abdennaceur Baghdad. "Accelerated model predictive controller for artificial pancreas." International Journal of Modelling, Identification and Control 30, no. 3 (2018): 229. http://dx.doi.org/10.1504/ijmic.2018.095335.

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