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Extended Kalman filter state estimation-based nonlinear explicit model predictive control design for blood glucose regulation of type 1 diabetic patient

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第5期60卷 1347-1361页

作者： Acharya, Debasis Das, Dushmanta Kumar Natl Inst Technol Nagaland Dimapur India

The computational burden of iterative online optimization-based model predictive control (MPC) process is solved by adapting off-line optimization-based nonlinear explicit model predictive control (NEMPC). In this paper, the application of NEMPC is verified to regulate blood glucose level in type 1 diabetes mellitus (T1DM) patients. The objective of glucose regulation is to avoid hyperglycemia (>180 mg/dl) and hypoglycemia (<50 mg/dl) by maintaining glucose level in the range of 70 to 180 mg/dl. It helps to avoid time complexity of iterative process during solution of optimization stage in MPC. In the nonlinear T1DM model, only the state dynamic of sugar is measurable with low complexity and high cost among other states of the model. Therefore, an extended Kalman filter (EKF) is used developed to estimate unavailable states. The information of the estimated states are used to develop the proposed control approach for the T1DM patients. The simulation results of the NEMPC along with EKF-based state estimator for T1DM model shows the regulation of blood glucose-level (BGL) within 70 to 180 mg/dl within 120 min. The robustness of the proposed scheme is also verified under the change in parameters and food disturbance. The control variability grid analysis (CVGA) of NEMPC for 50 numbers of virtual T1DM patients under random parametric changes and meal disturbances shows the avoidance of hypoglycemia and hyperglycemia in type 1 diabetic patients. The proposed control method is simple, robust and efficient to regulate glucose level in T1DM patients.

关键词： Blood glucose concentration (BGC) Type 1 diabetes mellitus (T1DM) Hyperglycemia Hypoglycemia Blood glucose regulation explicit model predictive control Extended Kalman filter

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Real-Time Energy-Efficient control for Fully Electric Vehicles Based on an explicit model predictive control Method

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第6期67卷 4693-4701页

作者： Zhang, Shuwei Luo, Yugong Li, Keqiang Li, Victor Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

With zero emissions and high energy efficiency, the fully electric vehicle (FEV) is universally becoming more popular. However, limited driving range is one of the primary weaknesses of FEVs. Recently, with the rapid development of intelligent transportation systems, various types of traffic information are available to the vehicle control system, and energy-efficient control of FEVs can be realized based on this information to further extend the total driving range. In this paper, the movement of a preceding vehicle is predicted within each control horizon with vehicle-to-vehicle wireless communication technology, and a better understanding of the future movement of preceding vehicles is established. An explicit model predictive control (EMPC) method is developed to realize real-time control, with the objective of reducing driving energy consumption while maintaining a suitable distance between the preceding vehicles. Dynamic driving simulator is utilized to collect driving data and showcase the effectiveness of the EMPC method. Simulation results show that the proposed real-time control method improves driving energy efficiency in most scenarios and better energy saving results can be observed.

关键词： Fully electric vehicles energy efficient control explicit model predictive control

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Real-Time Optimal Lithium-Ion Battery Charging Based on explicit model predictive control

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第2期17卷 1318-1330页

作者： Tian, Ning Fang, Huazhen Wang, Yebin Univ Kansas Dept Mech Engn Lawrence KS 66045 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA

The rapidly growing use of lithium-ion batteries across various industries highlights the pressing issue of optimal charging control, as charging plays a crucial role in the health, safety, and life of batteries. The literature increasingly adopts model predictive control (MPC) to address this issue, taking advantage of its capability of performing optimization under constraints. However, the computationally complex online constrained optimization intrinsic to MPC often hinders real-time implementation. This article is thus proposed to develop a framework for real-time charging control based on explicit MPC (eMPC), exploiting its advantage in characterizing an explicit solution to an MPC problem, to enable real-time charging control. This article begins with the formulation of MPC charging based on a nonlinear equivalent circuit model. Then, multisegment linearization is conducted to the original model, and applying the eMPC design to the obtained linear models leads to a charging control algorithm. The proposed algorithm shifts the constrained optimization to offline by precomputing explicit solutions to the charging problem and expressing the charging law as piecewise affine functions. This drastically reduces not only the online computational costs in the control run but also the difficulty of coding. Extensive numerical simulation and experimental results verify the effectiveness of the proposed eMPC charging control framework and algorithm. The research results can potentially meet the needs for real-time battery management running on embedded hardware.

关键词： Computational modeling Integrated circuit modeling Real-time systems Batteries Optimization Numerical models Load modeling Equivalent circuit model explicit model predictive control health-aware charging lithium-ion battery real-time charging

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Multi-Resolution explicit model predictive control: Delta-model Formulation and Approximation

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IEEE TRANSACTIONS ON AUTOMATIC control 2013年第11期58卷 2979-2984页

作者： Bayat, Farhad Johansen, Tor Arne Univ Zanjan Dept Engn Control Syst Grp Fac Elect Engn Zanjan *** Iran Norwegian Univ Sci & Technol Dept Engn Cybernet Ctr Autonomous Marine Operat & Syst N-7491 Trondheim Norway

This paper deals with the explicit solution and approximation of the constrained linear finite time optimal control problem for systems with fast sampling rates. To this aim, the recently developed explicit model predictive control (eMPC) is reformulated and characterized using the delta-operator to enjoy its promising advantages compared to the time-shift operator. Using the proposed delta-model eMPC formulation, a systematic method is proposed for first designing a low-complexity approximate eMPC solution and then improving its closed loop action without first determining an exact optimal solution that might be of prohibitive complexity. It is shown that the stability and feasibility of the proposed sub-optimal solution is guaranteed.

关键词： Approximate explicit model predictive control delta-operator explicit model predictive control multi-parametric programming multi-resolution model predictive control

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On the performance of observer-based explicit model predictive control

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2015年第6期37卷 769-779页

作者： Bayat, Farhad Univ Zanjan Dept Engn Zanjan *** Iran

This paper addresses the performance recovery of explicit model predictive control (eMPC) when states of the system are not available via measurement. It is shown that the performance of the closed-loop system under the eMPC controller can be degraded when using a state observer instead of measurement. To moderate this degradation, an optimization-based approach is proposed that enables the designer to systematically design an optimal observer. It is proved that the proposed approach leads to the maximum achievable performance (in some sense) when compared to the desired performance (i.e. when states are available via measurement). Simulation results illustrate the performance and effectiveness of the proposed approach compared to a set of observers designed using a robust pole-placement approach.

关键词： explicit model predictive control output feedback eMPC multi-parametric programming performance optimization

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Application of explicit model predictive control to a vehicle semi-active suspension system

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE control 2020年第3期39卷 772-786页

作者： Houzhong, Zhang Jiasheng, Liang Chaochun, Yuan Xiaoqiang, Sun Yingfeng, Cai Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China

The vehicle semi-active suspension is a typical multiple-input multiple-output system with strong couplings, actuator constraints and fast dynamics. This paper addresses the damping force regulation of shock-absorber in vehicle semi-active suspensions using an explicit model predictive control (EMPC) approach, which allows minimizing the system control objective function while satisfying the actuator constraints. The main advantage of the proposed approach is that the control law computation requirement is low, and thus the EMPC system is suitable for implementation in a standard automotive microcontroller. The design of the EMPC system consists of mathematical modeling, objective function determination, controller formulation and simulation validation. Presented simulation results verify that a superior control performance of the vehicle semi-active suspension system is achieved by the proposed EMPC control approach compared with the performance obtained using conventional control method.

关键词： Semi-active suspension explicit model predictive control damping force regulation actuator constraint

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explicit model predictive control through robust optimization

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AICHE JOURNAL 2023年第10期69卷 e18172-e18172页

作者： Pappas, Iosif Diangelakis, Nikolaos A. Pistikopoulos, Efstratios N. Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77843 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77843 USA Tech Univ Crete Sch Chem & Environm Engn Khania Greece

A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete-time uncertain linear systems is presented. We consider uncertain processes, affected by box-bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study.

关键词： explicit model predictive control multiparametric programming optimization under uncertainty robust optimization

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Improving the air supply and hydrogen dilution control of a fuel cell electric vehicle with explicit model predictive control

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年第PartA期52卷 1236-1247页

作者： Eisner, Maximilian K. Zhou, Zheng Formanski, Volker Kennel, Ralph M. Tech Univ Munich Sch Engn & Design TUM Arcisstr 21 D-80333 Munich Bavaria Germany BMW Grp Dept Fuel Cell Petuelring 130 D-80809 Munich Bavaria Germany

This study demonstrates the applicability of an explicit model predictive controller (ExMPC) to the air mass flow control in a fuel cell electric vehicle. The two control tasks comprise the supply of air to the fuel cell stack for the electrochemical reaction as well as the dilution of hydrogen in the outlet gas flow of the cathode subsystem. With the aim of achieving zero control deviation for steady states, the linear model in the state observer is extended with two perturbations. During the experimental validation of the proposed controller, fast dynamics are achieved with the ExMPC, which generally exploits the limits of the control variables. After the calibration of the ExMPC, the final comparison to a set of reference PI controllers reveals a significant improvement in the closed-loop behavior of the cathode subsystem during step changes in the controller setpoints.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： explicit model predictive control Fuel cell electric vehicle Fuel cell system control Automotive application

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A novel traffic signal split approach based on explicit model predictive control

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MATHEMATICS AND COMPUTERS IN SIMULATION 2019年 155卷 105-114页

作者： Lu, Ke Du, Pingping Cao, Jinde Zou, Qiming He, Tianjia Huang, Wei Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Anhui Univ Technol Sch Management Sci & Engn Maanshan 243032 Peoples R China Southeast Univ Sch Math Nanjing 210096 Jiangsu Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Southeast Univ Intelligent Transportat Syst Res Ctr Nanjing 210096 Jiangsu Peoples R China

As a consequence of the rapid growth of vehicles in cities, urban traffic congestion has become more and more serious nowadays. As an effective control approach, model predictive control (MPC) has been deeply studied for transportation management. However, the relatively complex on-line computing hinders MPC from being further applied on transportation system, especially for large scale transportation networks. Based on the refined store-and-forward model, we propose a signal split control approach with explicit model predictive control (EMPC) in this paper, by which the complexity of online optimization can be significantly reduced. With multi-parametric Quadratic Program (mp-QP), our approach can generate explicit signal control law, and then shifts the former repeated online signal split optimization offline. As a result, the real time signal control becomes an easier task by searching the lookup table corresponding to the transportation state. The simulation experiments demonstrate that our EMPC based approach outperforms the traditional fix-time signal control. Meanwhile, compared with traditional MPC based approach, our approach can decrease the total computation complexity, and then increase the applicability for real transportation system. (C) 2017 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： explicit model predictive control Signal split Traffic network Intelligent transportation system

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Perspectives in Multiparametric Programming and explicit model predictive control

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AICHE JOURNAL 2009年第8期55卷 1918-1925页

作者： Pistikopoulos, E. N. Univ London Imperial Coll Sci Technol & Med Dept Chem Engn Ctr Proc Syst Engn London SW7 2AZ England

Abstract not available.

关键词： explicit model predictive control multi-parametric programming multi-parametric control robust optimization

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