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An explicit predictive controller for fuel-cell electric vehicles incorporating the hierarchical architecture

APPLIED ENERGY 2025年 383卷

作者： Jiang, Zewei Hou, Zhuoran Chu, Liang Zhao, Di Jiang, Jingjing Yang, Jun Zhang, Yuanjian Jilin Univ Coll Automot Engn Changchun 130022 Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU England

In light of the severity of global climate change and the energy crisis, fuel cell electric vehicles (FCEVs) have garnered significant attention as a potential solution. Integrating advanced energy management strategies (EMSs) into FCEVs can effectively optimize the energy consumption performance of the highly nonlinear powertrain across various driving conditions. In this paper, a hierarchical explicit model predictive control energy management strategy (H-EMPC EMS) is proposed to enhance the economic performance of FCEVs and ensure control robustness across diverse driving conditions. Firstly, a hierarchical control architecture is developed to improve the adaptability of the EMS to diverse driving conditions. The upper layer performs driving conditions recognition and operation modes switching by observing the vehicle state. The lower layer determines power distribution throughout the powertrain. Secondly, an explicit MPC (eMPC) controller is developed to make the powertrain promptly respond to the power demand for the vehicle. To bolster the real-time application capability of the controller, systematic optimization is carried out by combining eMPC offline control law generation and online control law invocation. Thirdly, a feedback compensator is integrated into the controller to mitigate the effects of parameter variations on control and strengthen the robustness of the EMS. Finally, simulation evaluations and hardware-in-the-loop (HIL) tests demonstrate that the proposed H-EMPC EMS can improve economic performance and guarantee real-time performance for the studied FCEV. Compared with other baselines, the energy-saving capability is remarkable, showcasing its promising performance.

关键词： Energy management strategy explicit model predictive control Fuel cell electric vehicle Hierarchical control architecture Adaptive optimization

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A Pontryagin Principle-Based Frequency Governor for Constrained Computing Systems

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2024年第4期32卷 1442-1450页

作者： Rao, Sachit Malpani, Nishant Rao, Shrisha Int Inst Informat Technol Bangalore IIIT B Bangalore 560100 India Aeva Percept Bangalore 560093 India

Management of power is a crucial problem in computing systems where power is finite, processor performance and energy needs are high, and thermal constraints have to be respected. The trade-off between performance and energy expenditure is well recognized. To satisfy these conflicting requirements, in this article, a dynamic system framework is adopted, and results from optimal control theory, notably Pontryagin's Minimum Principle (PMP), are applied to derive an energy-optimal (EO) time-varying processor speed law, or frequency governor, to execute assigned tasks. PMP is chosen as it allows for system input constraints as well as thermal and power budget constraints to be considered;the PMP-based governor is also compared with a model predictive controller (MPC) implemented by following the explicit-MPC framework using a linear model. The main contributions of this article are 1) determining an empirical time-invariant nonlinear dynamic model of an Intel CPU with task execution rate, power consumption, and temperature as the outputs, and clock frequency as the input;2) the Linux implementation of a PMP-based clock frequency governor on the CPU based on a linear model as well as the nonlinear model;and 3) hardware implementation of an explicit-MPC on the same platform using the frequency schedule derived from linear model simulations. Limits on task completion times and energy savings achieved in the execution of three benchmark tasks: MiBench, LINPACK, and Sorting positive integers, are presented. Experimental results show that it is possible to reduce energy consumption with an increase in task execution time while executing these benchmark tasks;it is also shown that it is possible to tune the PMP and MPC parameters to obtain similar performances. The approach presented in this article can be applied to design optimal controllers for other types of stand-alone or heterogeneous computing systems.

关键词： Task analysis Frequency control Time-frequency analysis Computational modeling Servers Process control Clocks Energy-aware computing explicit model predictive control optimal control theory Pontryagin's minimum principle (PMP) power estimation and optimization

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explicit model predictive controller Design for Thickness and Tension control in a Cold Rolling Mill

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IFAC-PapersOnLine 2016年第20期49卷 126-131页

作者： Ogasahara, Tomoyoshi Hovd, Morten Asano, Kazuya Instrument and Control Engineering Research Dept. Steel Research Lab. JFE Steel Corp. FukuyamaHiroshima721-8510 Japan Department of Engineering Cybernetics Norwegian University of Science and Technology TrondheimN-7491 Norway Steel Research Lab. JFE Steel Corp. KawasakiKanagawa210-0855 Japan

This paper addresses modeling of a cold rolling mill and controller design based on explicit model predictive control(explicit MPC). The control objectives are to track the exit thickness to the reference with high accuracy with minimized strip tension deviation. In the simulation, good control is obtained when the disturbance entering the system is small enough. However, the thickness shows the offset from the reference in case of acceleration, because the approximation error of the linearized model increases and it is different from the model in the design of the controller. In order to compensate for the control offset, an additional integral logic using the estimation error of exit thickness is proposed. The validity of this approach was verified by simulation and offset free control was achieved. © 2016

关键词： controllers Cold rolling Cold rolling mills Iron and steel industry MIMO systems model predictive control predictive control systems Process control Steelmaking Approximation errors control objectives controller designs explicit model predictive control Multi input/multi output systems Optimal controls predictive control Process model

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explicit model predictive control of Buck Converter

Explicit Model Predictive Control of Buck Converter

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15th International Power Electronics and Motion control Conference and Exposition (EPE-PEMC ECCE Europe)

作者： Vlad, C. Rodriguez-Ayerbe, P. Godoy, E. Lefranc, P. Supelec E3S Automat Dept Gif Sur Yvette France Suplec Dept Energy Gif Sur Yvette France

ISBN: (纸本)9781467319720

This article proposes a model predictive control (MPC) design to control a fixed frequency DC-DC power converter in a defined operating domain. The prediction model used for the control synthesis is a piecewise affine approximation (PWA) that covers the whole operating range of the converter. Based on the obtained hybrid model a constrained optimal control problem is formulated and solved. The explicit form of the control law is derived offline as an affine state feedback controller. This explicit controller is stored in a look-up table for implementation. The experimental results show the potential advantages of this control approach in comparison with a static state-feedback or a classic PI controller.

关键词： explicit model predictive control hybrid systems Buck converter

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Polytopic Approximation Algorithm of explicit model predictive control

Polytopic Approximation Algorithm of Explicit Model Predicti...

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32nd Chinese control Conference (CCC)

作者： Zhang Ju Ma Yanfang He Xuehua Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9781479900305

Polytopic approximation of explicit model predictive control is an algorithm that compute approximate explicit control laws that trade-off complexity against approximation error for MPC controllers. The approximate method has overcome the shortcomings of explicit model predictive control, reduced the complexity and improved the speed of offline calculation, increased the real-time control action. In this paper, we make a comparison between the polytopic approximate algorithm and the explicit model predictive control algorithms by the simulation of several numerical examples. The advantages of polytopic approximation algorithm are concluded.

关键词： explicit model predictive control polytopic approximation simulation of numerical examples

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explicit hybrid MPC for the lateral stabilization of electric vehicle system

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ENERGY REPORTS 2024年 11卷 1100-1111页

作者： Yaakoubi, Hanen Haggege, Joseph Rezk, Hegazy Al-Dhaifallah, Mujahed Univ Tunis El MANAR Natl Engn Sch Tunis ENIT Res Lab Automat Control LARA BP 37Le Belvedere Tunis 1002 Tunisia Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals IRC Sustainable Energy Syst IRC SES Dhahran 31261 Saudi Arabia

This paper presents a hybrid model predictive control (hMPC) approach to improve Electric Vehicle (EV) stability when cornering or in high-risk driving conditions. By using approximations of tire force characteristics, the EV system proposed in this study is considered as a Piecewise Affine (PWA) system which is a class of Hybrid Dynamical Systems (HDSs). First, a hybrid modeling of the studied system is developed within the PWA framework. Then, the synthesis of an explicit hybrid MPC law, based on the integration of the Active Front-wheel Steering control method (AFS) and Direct Yaw moment control method (DYC), can be established. The purpose of the PWA-MPC is to force the yaw rate and sideslip angle of the EV system to follow the given references to guarantee lateral stability. The calculation of the control laws of this proposed approach is performed offline using multiparametric programming. Finally, this paper investigates the effectiveness of the designed controller by performing a set of simulations to analyze the efficiency and robustness of the proposed approach. A comparative study is addressed in this work to demonstrate the efficiency of the proposed hybrid MPC controller compared to the conventional MPC approach which considers the linear model of the system. Another simulations are carried out to assess the robustness of the proposed control, important disturbances and uncertainties were incorporated such as the change of the additional mass, the change of terrain and the high velocity. The simulation results show that the PWA EV system model with the designed controller is able to ensure lateral stability by keeping the sideslip angle and yaw rate tracking the ideal values.

关键词： Electric vehicle Piecewise-affine system Lateral stability control explicit model predictive control Multiparametric mixed integer linear programs Active front-wheel steering control method Direct yaw moment control method. Branch and bound algorithm

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Integrating model predictive control and deep learning for the management of an EV charging station

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年第PartB期224卷 33-48页

作者： D'Amore, G. Cabrera-Tobar, A. Petrone, G. Pavan, A. Massi Spagnuolo, G. Univ Salerno Dept Informat & Elect Engn & Appl Math Via Giovanni Paolo II 13 I-84084 Salerno Italy Univ Trieste Dept Engn & Architecture Piazzale Europa 1 I-34127 Trieste Italy Univ Trieste Ctr Energy Environm & Transport Giacomo Ciamician Piazzale Europa 1 I-34127 Trieste Italy Polytech Univ Milan Dept Energy Via Raffaele Lambruschini I-20156 Milan Italy

explicit model predictive control (EMPC) maps offline the control laws as a set of regions as a function of bounded uncertain parameters using multi-parametric programming. Then, in online mode, it seeks the best solution within these areas. Unfortunately, the offline solution can be computationally demanding because the number of regions can grow exponentially. Thus, this paper presents the application of a deep neural network (DNN) to learn the EMPC's regions for a photovoltaic-based charging station. The main uncertain parameters in this study are the forecast error of photovoltaic power production and the battery's state of charge. Additionally, the connection or disconnection of an electric vehicle is considered a disruption. The final controller creates the regions at the start of each prediction time or when a disruption occurs, only using the previously created DNN. The obtained solution is validated using data from an e-vehicle charging station installed at the University of Trieste, Italy. (c) 2023 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Optimization Energy management Electric vehicle (EV) Photovoltaic (PV) model predictive control explicit model predictive control Uncertainties Deep neural network Deep neural network

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explicit model predictive controller under uncertainty: An adjustable robust optimization approach

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JOURNAL OF PROCESS control 2019年 84卷 115-132页

作者： Tejeda-Iglesias, Manuel Lappas, Nikolaos H. Gounaris, Chrysanthos E. Ricardez-Sandoval, Luis Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

Conventional model predictive control (MPC) involves solving an optimization problem online to determine the control actions that minimize a performance criterion function. The high computational expense associated with conventional MPC may make its application challenging for large-scale systems. explicit MPC has been developed to solve the optimization problem offline. In this work, adjustable robust optimization is used to obtain the explicit solution to the MPC optimization problem offline for discrete-time linear time invariant systems with constraints on inputs and states. In the robust model formulation, an uncertain additive time-varying error is introduced to account for model uncertainty resulting from plant-model mismatch caused by un-measurable disturbances or process nonlinearities. The explicit solution is an optimal time-varying sequence of feedback control laws for the control inputs parameterized by the systems states. The control laws are evaluated in a time-varying manner when the process is online using state measurements. This study shows that the resulting control laws ensure the implemented control actions maintain the system states within their feasible region for any realizations of the uncertain parameters that are within the user-defined uncertainty set. Three case studies are presented to demonstrate the proposed approach and to highlight the benefits and limitations of this method. The proposed framework advances the development of explicit MPC by further expanding its application to large as well as nonlinear systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： explicit model predictive control Adjustable robust optimization Robust control

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Design of Approximate explicit model predictive controller Using Parametric Optimization

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JOURNAL OF MECHANICAL DESIGN 2022年第12期144卷 124501-124501页

作者： Tsai, Ying-Kuan Malak, Richard J., Jr. Texas A&M Univ Dept Mech Engn Design Syst Lab College Stn TX 77843 USA

This paper introduces a new technique, called state-parameterized nonlinear programming control (sp-NLPC), for designing a feedback controller that can stabilize intrinsically unstable nonlinear dynamical systems using parametric optimization. Stability-preserving constraints are included in the optimization problem solved offline by the predictive parameterized Pareto genetic algorithm (P3GA), a constrained nonlinear parametric optimization algorithm. The optimal control policy is approximated from P3GA output using radial basis function (RBF) metamodeling. The sp-NLPC technique requires fewer assumptions and is more data-efficient than alternative methods. Two nonlinear systems (single and double inverted pendulums on a cart) are used as benchmarking problems. Performance and computational efficiency are compared to several competing control design techniques. Results show that sp-NLPC outperforms and is more efficient than competing methods. A qualitative investigation on phase plane analysis for the controlled systems is presented to ensure stability. The approximating state-dependent solution for the control input lends itself to applications of control design for control co-design (CCD). Such extensions are discussed as part of future work.

关键词： design optimization explicit model predictive control parametric optimization

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Real-time process safety and systems decision-making toward safe and smart chemical manufacturing

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DIGITAL CHEMICAL ENGINEERING 2025年 15卷

作者： Braniff, Austin Akundi, Sahithi Srijana Liu, Yuanxing Dantas, Beatriz Niknezhad, Shayan S. Khan, Faisal Pistikopoulos, Efstratios N. Tian, Yuhe West Virginia Univ Dept Chem & Biomed Engn Morgantown WV 26506 USA Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77845 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77845 USA Texas A&M Univ Mary Kay OConnor Proc Safety Ctr Artie McFerrin Dept Chem Engn College Stn TX USA

The ongoing digital transformation has created new opportunities for chemical manufacturing with increasing plant interconnectivity and data accessibility. This paper reviews state-of-the-art research developments which offer the potential for real-time process safety and systems decision-making in the digital era. An overview is first presented on online process safety management approaches, including dynamic risk analysis and fault diagnosis/prognosis. Advanced operability and control methods are then discussed to achieve safely optimal operations under uncertainty (e.g., flexibility analysis, safety-aware control, fault-tolerant control). We highlight the connections between systems-based operation and process safety management to achieve operational excellence while proactively reducing potential safety losses. We also review the developments and showcases of digital twins paving the way to actual cyber-physical integration. Outstanding challenges and opportunities are identified such as safe data-driven control, integrated operability, safety and control, cyber- physical demonstration, etc. Toward this direction, we present our ongoing developments of the REal-Time Risk-based Optimization (RETRO) framework for safe and smart process operations.

关键词： Real-time optimization explicit model predictive control Dynamic risk analysis Fault diagnosis and prognosis Digital twin Cyber-physical integration

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