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2024 Vehicle Power and Propulsion Conference

作者： Li, Kaibo Dinh, Truong Yao, Kairui

ISBN: (纸本)9798331541613;9798331541606

model predictive control (MPC) stands out as a promising approach for Battery Thermal Management (BTM). However, due to large thermal inertia of batteries, achieving optimal control of BTM necessitates longer prediction horizons, leading to increased computational loads and uncertainties. This study aims to address these challenges by developing computationally efficient MPC solutions for BTM. Two approaches, adaptive MPC (AMPC) and Hierarchical MPC (H-MPC), are proposed alongside the baseline Nonlinear MPC (NLMPC). NLMPC employs linearization at the nominal operating point and updates state parameters online, thereby simplifying model complexity. Through AMPC, both accumulation constraint violation and execution time are notably reduced, by up to 15% and 76%, respectively. On the other hand, H-MPC utilizes one NLMPC in the planning layer and another one in the operating layer. The optimal battery temperature from the MPC in the planning layer serves as a reference for the MPC in the operating layer. Notably, the two NLMPC in the two layers adopt different prediction horizons and sampling times. Simulation results demonstrate that H-MPC achieves significant reductions in BTM consumption and execution time, by up to 8% and 89%.

关键词： battery thermal management model predictive control adaptive model predictive control hierarchical model predictive control electric vehicle

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A comparison between gain-scheduling linear quadratic regulator and model predictive control for a manipulator with flexible components

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND control ENGINEERING 2022年第7期236卷 1285-1293页

作者： Colombo, Fernanda Thais da Silva, Maira Martins Univ Sao Paulo Sao Carlos Sch Engn Dept Mech Engn Trabalhador Sao Carlense 400 BR-13566590 Sao Carlos Brazil

Dynamic performance and energy efficiency can be improved by reducing the inertia of mechanical systems. However, the components of such a system may be flexible and, inevitably, subject to vibrations. Moreover, the relative motion between flexible components leads to time-varying boundary conditions characterizing configuration-dependent dynamics. Since robust controllers may be conservative for this problem, adaptive controllers should be considered since they can cope with performance design requirements. In this work, we present a comparison between a linear quadratic regulator gain-scheduling control and an adaptive model predictive control for a flexible industrial Cartesian manipulator with configuration-dependent dynamics. This study takes the system stability and the use of bounded actuation into account. Numerical results demonstrate that the adaptive model predictive control strategy achieved better performance than the linear quadratic regulator gain scheduled. Also, in applications with tighter actuation bounds, the difference between the performance of both approaches is even higher, reinforcing the model predictive control capability of dealing with limited actuation.

关键词： adaptive model predictive control linear quadratic regulator gain-scheduled control configuration-dependent dynamics linear parameter-varying systems adaptive control systems

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Modified predictive control of continuum manipulators with learning-based model

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INTERNATIONAL JOURNAL OF modelLING IDENTIFICATION AND control 2022年第1期40卷 44-58页

作者： Parvaresh, Aida Moosavian, S. Ali A. KN Toosi Univ Technol Dept Mech Engn Ctr Excellence Robot & Control Adv Robot & Automated Syst ARAS Lab Tehran Iran

Continuum manipulators are considered as systems with high intrinsic complexities, nonlinearities and uncertainties, which encounter several problems in accurate modelling and control purposes. However, model predictive control (MPC) strategy is highly dependent on the accuracy of the associated model used by the controller. Accordingly, any mismatch in parameters/structure of the model would result in degraded performance of MPC. To alleviate accuracy-related problems, data-driven adaptive model predictive control (DD-AMPC) is proposed. The proposed approach is based on deriving an explicit linearised model from data-driven identification, which is appropriate for model-based control purposes. In this approach, the continuum manipulator can be modelled and controlled without the requirement for comprehensive knowledge of the physical system. To reduce computational cost, receding horizon concept is modified by parameterising control trajectory. The proposed scheme is implemented and its functionality in point-to-point tracking, path following and presence of disturbances is assessed. Additionally, its superiority and effectiveness over conventional control schemes is evaluated and confirmed.

关键词： adaptive model predictive control AMPC learning-based modelling continuum manipulators system identification data-driven identification

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Robust Implementation of the model predictive control for DC-DC Power Converters and Industrial control Applications

Robust Implementation of the Model Predictive Control for DC...

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作者： Albira, Mohamed Emhemed Oakland University

学位级别：Ph.D., Doctor of Philosophy

This dissertation addresses the development of the robustness and strength of the model predictive control algorithm subjected to input constraints for a plant system with and without parameters' uncertainties. In the beginning, the MPC control system was implemented for systems with no types of parameters' uncertainties. The proposed system models were stable and linear and all of its parameters were fully known. They were formulated in model state-space system format. The main objective of this control system design was to maintain a smooth and constant output signal that could easily track the assigned desired output signal. The technical process of this control design was to calculate the optimal solutions for the proposed plant system by optimizing the Quadratic programming problem (QP) subjected to linear inequality constraints. Therefore, the proposed control system successfully forced the outputs of the proposed systems to track the output reference signals in a fast response and with very small steady-state errors, even with the change in prediction horizon values. The second control system approach was for a system model which assumed its parameters’ uncertainties. In the other words, the parameters were not precisely known, but they were bounded in a minimum and maximum range. The parameters' uncertainties and the converter's switching behaviors made it act as a highly nonlinear system. Therefore, the adaptive model predictive controller (AMPC) with the Linear Parameter Varying (LPV) control algorithm was implemented to address these issues and to secure a sustainable output signal with on types of noise or degradations. In this algorithm, the LPV model was created out of a set of Linear Time-Invariant (LTI) models, which are used to update the AMPC controller based on the feedback signals that come from the plant model and the change in the system parameters. Due to the changes in the plant model parameters over time, the AMPC was the perfect control app

关键词： adaptive model predictive control Buck boost buck-boost converters control system design Dc-dc power electronics Linear model predictive control Linear parameter varying

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A moving obstacle avoidance strategy-based collaborative formation for the bionic multi-underwater spherical robot control system

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OCEAN ENGINEERING 2025年 329卷

作者： Li, Ao Guo, Shuxiang Li, Chunying Yin, He Liu, Meng Shi, Liwei Beijing Inst Technol Beijing 100081 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Guangdong Peoples R China

The collaborative operation of multi-underwater robot formation is an effective way to deal with the complex underwater environment. A formation control strategy with high efficiency and accuracy in moving obstacle avoidance is very important. Based on the multi-robot experiment platform, a moving obstacle avoidanceconstrained adaptive model predictive control (MOAC-AMPC) strategy is proposed for Underwater Spherical Robots (USRs). The strategy is optimized in two main aspects: Firstly, based on model predictive control, an adaptive weight matrix based on tracking error is designed to solve the tedious parameter tuning problem and reduce the tracking time. Then, the Velocity Obstacle (VO)-based dynamic constraints are designed to avoid multiple moving obstacles. Finally, the multi-underwater spherical robots experiment platform is built. Pool experiments are set up on the platform to verify the multi-robot formation with obstacles avoidance. The feasibility and superiority of the proposed strategy are verified by simulations and experiments. The application of the proposed multi-USRs strategy has certain practical value in multi-robot trajectory tracking and obstacle avoidance.

关键词： Collaborative formation control strategy Multiple moving obstacle avoidance Multi-underwater spherical robots Trajectory tracking adaptive model predictive control Velocity obstacle (VO)-Based dynamic constraints

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Supervisor-Based Hierarchical adaptive MPC for Yaw Stabilization of FWID-EVs Under Extreme Conditions

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第2期26卷 1852-1863页

作者： Zhao, Jing Li, Renbin Zhang, Guoen Huang, Chao Liang, Zhongchao Ding, Zhengtao Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Northeastern Univ Foshan Grad Sch Innovat Foshan 560011 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL England

This work focuses on the yaw stabilization of the four-wheel-independent-drive electric vehicle (FWID-EV) with the constrained active front steering (AFS) and direct yaw-moment control (DYC). First, a modified tire model is employed in the design of the unscented Kalman filter to realize the estimation of the tire-road friction coefficient (TRFC), and a backpropagation neural network is developed to online estimate the tire cornering stiffness;Second, a yaw stabilization supervisor is designed to solve the conflicts between the AFS and DYC systems, and the mode-boundary maps of the tire operating regions are utilized to generate the triggered signals so as to activate the systems;Third, a hierarchical adaptive model predictive control (MPC), including the estimation, activation, compensation, and distribution layers is proposed for yaw stabilization of the FWID-EV under the extreme conditions. Emergency maneuvers under big path curvature, low TRFC, and high vehicle speed are designed. Both software-in-the-loop and hardware-in-the-loop tests are performed to examine the effectiveness and practicability of the proposed methods, respectively.

关键词： Tires Estimation Vehicle dynamics Stability analysis Real-time systems Accuracy Wheels adaptive systems Vectors predictive control yaw stabilization supervisor hierarchical strategy adaptive model predictive control

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Coordinated control of path tracking and yaw stability for distributed drive electric vehicle based on AMPC and DYC

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年第5期239卷 1745-1762页

作者： Wu, Dongmei Guan, Yuying Xia, Xin Du, Changqing Yan, Fuwu Li, Yang Hua, Min Liu, Wei Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan Peoples R China Foshan Xianhu Lab Adv Energy Sci & Technol Guangdong Lab Foshan Peoples R China Wuhan Univ Technol Hubei Res Ctr New Energy & Intelligent Connected V Wuhan Peoples R China Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA USA Tech Ctr Dongfeng Commercial Vehicle Wuhan Peoples R China Univ Birmingham Sch Engn Birmingham England Purdue Univ Sch Elect & Comp Engn W Lafayette IN USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Maintaining both path-tracking accuracy and yaw stability of distributed drive electric vehicles (DDEVs) under various driving conditions presents a significant challenge in the field of vehicle control. To address this limitation, a coordinated control strategy that integrates adaptive model predictive control (AMPC) path-tracking control and direct yaw moment control (DYC) is proposed for DDEVs. The proposed strategy, inspired by a hierarchical framework, is coordinated by the upper layer of path-tracking control and the lower layer of direct yaw moment control. Based on the linear time-varying model predictive control (LTV MPC) algorithm, the effects of prediction horizon and weight coefficients on the path-tracking accuracy and yaw stability of the vehicle are compared and analyzed first. According to the aforementioned analysis, an AMPC path-tracking controller with variable prediction horizon and weight coefficients is designed considering the change of vehicle speed in the upper layer. The lower layer involves DYC based on the linear quadratic regulator (LQR) technique. Specifically, the intervention rule of DYC is determined by the threshold of the yaw rate error and the phase diagram of the sideslip angle. Extensive simulation experiments are conducted to evaluate the proposed coordinated control strategy under different driving conditions. The results show that, under variable speed and low adhesion conditions, the vehicle yaw stability and path-tracking accuracy have been improved by 21.58% and 14.43%, respectively, compared to AMPC. Similarly, under high speed and low adhesion conditions, the vehicle yaw stability and path-tracking accuracy have been improved by 44.30% and 14.25%, respectively, compared to the coordination of LTV MPC and DYC. The results indicate that the proposed adaptive path-tracking controller is effective across different speeds. Furthermore, the proposed coordinated control strategy successfully enhances the vehicle stability while

关键词： Distributed drive electric vehicle coordinated control path tracking adaptive model predictive control direct yaw moment control

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Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC

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RENEWABLE ENERGY 2024年 226卷

作者： Fan, Guozhu Peng, Chunhua Wang, Xuekui Wu, Peng Yang, Yifan Sun, Huijuan East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Peoples R China State Grid Jiangxi Elect Power Co Ltd Extra High Voltage Branch Nanchang 330029 Peoples R China

The uncertainty of renewable energy makes the optimal scheduling of integrated energy systems (IES) challenging and complex. The paper suggests a novel two-stage optimized scheduling model based on distributionally robust adaptive model predictive control (DRAMPC), which effectively improves scheduling accuracy and efficiency while taking robustness and economy into account. In the day-ahead stage, the multi-objective distributionally robust optimization (MODRO) model is composed based on the imprecise dirichlet model (IDM), which incorporates robustness and operating cost metrics into the optimization objective to achieve synergistic optimization of robustness and economy. The adaptive step double-loop model predictive control (ASDL-MPC) utilizes the dual closed-loop feedback of renewable energy output prediction errors and operating cost prediction errors to adaptively adjust the rolling time step, correcting the day-ahead scheduling bias while improving scheduling efficiency. The model is resolved using the cross-entropy-radar scanning differential evolution (CERSDE) algorithm. The results show that the DRAMPC model can balance economy and robustness, improving the economy of IES by 2.27% while ensuring robustness. The ASDL-MPC intra-day rolling optimization further improves scheduling accuracy and also increases computational efficiency by 6.58%.

关键词： Integrated energy system Distributionally robust optimization Two -stage optimization Robustness adaptive model predictive control

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Integrated control method for path tracking and lateral stability of distributed drive electric vehicles with extended Kalman filter-based tire cornering stiffness estimation

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JOURNAL OF VIBRATION AND control 2024年第11-12期30卷 2582-2595页

作者： Qi, Gengxin Yue, Ming Shangguan, Jinyong Guo, Lie Zhao, Jian Dalian Univ Technol Dept Automot Engn 2 Linggong Rd Dalian 116024 Peoples R China Dalian Univ Technol Ningbo Inst Ningbo Peoples R China

Aiming at the lack of adaptability of vehicle parameters under extreme conditions, this paper proposes an integrated control method for path tracking and lateral stability of distributed drive electric vehicles based on tire cornering stiffness adaptive model predictive control (MPC) scheme. The control method integrates active front steering and direct yaw control to improve the path tracking and lateral stability performance of distributed drive electric vehicles. Firstly, considering the influence of vertical load transfer, the tire cornering stiffness is estimated based on the extended Kalman filter (EKF) algorithm. Then, using this online updated tire cornering stiffness value, an adaptive MPC controller for path tracking and lateral motion stability of distributed drive electric vehicles is constructed. Meanwhile, a fuzzy sliding mode control (Fuzzy-SMC)-based longitudinal velocity controller is established to ensure the accuracy of velocity tracking. Also, according to the distributed driving characteristics of the controlled system, a tire torque distributor based on weighted pseudo-inverse (WPI) is designed with the minimum tire load rate as the optimization objective, where the road adhesion condition and the maximum output torque of the motor are considered as constraints. The simulation results show that the proposed integrated control method based on tire cornering stiffness adaptive model predictive control is robust and effective. Compared with constant cornering stiffness model predictive control-based control method, it can improve the vehicle path tracking accuracy and lateral motion stability under extreme conditions.

关键词： tire cornering stiffness extended Kalman filter adaptive model predictive control vehicle lateral stability path tracking

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Formation control of UAV-USV based on distributed event-triggered adaptive MPC with virtual trajectory restriction

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OCEAN ENGINEERING 2024年 294卷

作者： Zhu, Yakun Li, Shilong Guo, Ge Yuan, Pengfei Bai, Jianguo Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

This paper studies the formation and trajectory tracking problem of unmanned aerial vehicle -unmanned surface vehicle (UAV-USV). A distributed event -triggered adaptive model predictive control (DEAMPC) method is constructed for the formation control by using information of the adjacent vehicles. First, the preset formation equation and parameter change are incorporated into the construction process of the cost function to achieve the formation control. Second, in order to reduce the calculation times of the optimization problem, an eventtriggered mechanism is proposed. Finally, the virtual trajectory restriction is designed to deal with the condition of interrupted communication network. Moreover, some simulation results are given to illustrate the advantages and effectiveness of all the above methods.

关键词： Formation control Trajectory tracking Event-trigger adaptive model predictive control

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