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Driver-centric data-driven robust model predictive control for mixed vehicular platoon

NONLINEAR DYNAMICS 2023年第22期111卷 20975-20989页

作者： Wu, Yanhong Zuo, Zhiqiang Wang, Yijing Han, Qiaoni Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Intelligent Unmanned Swarm Technol Tianjin 300072 Peoples R China

The penetration rate of automated vehicles (AVs) may remain unsaturated for a long time, resulting in the coexistence of AVs and human-driven vehicles (HDVs). The non-ideal driving behavior has inherent uncertainties and randomness that would cause traffic congestion and oscillation. To improve the driving safety and comfort of mixed vehicular platoon (MVP) consisting of HDVs and AVs, this paper proposes a driver-centric data-driven robust model predictive control (DDRMPC) strategy. This strategy involves two new elements of a data-driven robust model predictive controller and the personalized driving policy. A data-driven MVP model is established with subspace identification to alleviate the adverse effects of uncertain dynamics. To provide a driver-centric driving experience, a personalized driving policy with a flexible spacing corridor and soft constraints is designed in terms of different driving styles. In this way, a tube-based robust model predictive controller is integrated with the data-driven MVP model to develop the DDRMPC strategy, and its stability is proven. Moreover, four experiments with thirty-seven drivers are carried out on a self-developed MVP platform. Finally, several experiments with MVP demonstrate the effectiveness of the proposed DDRMPC strategy.

关键词： Mixed vehicular platoon Driver-centric Data-driven model robust model predictive control

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Optimal oxygen excess ratio tracking of proton exchange membrane fuel cell with uncertainty based on robust model predictive control strategy

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FUEL CELLS 2023年第5期23卷 338-352页

作者： Zhang, Xin Zhang, Chunlei Zhang, Zhijin Li, He Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China

To address the challenge of tracking the optimal oxygen excess ratio (OER) in the cell stack cathode of the proton exchange membrane fuel cell (PEMFC), which is subject to norm-bounded parameter uncertainty and frequent external perturbations, this manuscript provides a novel robust model predictive control strategy with a state feedback control structure and hard constraints. We aim to minimize the tracking error of OER while maximizing the net power under varying operational conditions of the PEMFC. Initially, a control-oriented model for the air supply system is built and its accuracy is validated by comparing it with experimental results. Next, we construct the discrete state-space equations for optimal control and employ the linear matrix inequality (LMI) method to transform the problem of difficult min-max optimization solutions into a convex optimization problem with LMI constraints. Finally, the effectiveness of the proposed control algorithm is validated through simulation. The results demonstrate the strong robustness and superiority of the proposed controller against uncertainties in plant parameters for robust OER trajectory tracking. It improves the transient performance through robust precision tracking applications, which will eventually accelerate PEMFC commercialization.

关键词： convex optimization optimal OER tracking PEMFC robust model predictive control uncertainty

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robust model predictive control of uncertain nonlinear time-delay systems via control contraction metric technique

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INTERNATIONAL JOURNAL OF robust AND NONLINEAR control 2021年第8期31卷 3330-3353页

作者： Duan, Zhengyi Wei, Caisheng Xie, Wei Southeast Univ Sch Automat 2 Sipailou Nanjing Jiangsu Peoples R China Cent South Univ Sch Aeronaut & Astronaut Changsha 410083 Peoples R China Chinese Acad Sci Haixi Inst Jinjiang Peoples R China

This article is concerned with the problem of robust model predictive control (MPC) for uncertain nonlinear time-delay systems. In order to reduce the computational conservativeness of the existing robust MPC given in the min-max optimization formulation, a novel tube-based MPC method is investigated via exploring MPC and control contraction metric (CCM) technique. Based on the Kyapunov-Krasovskii arguments, the MPC is designed as a nominal controller to generate a reference trajectory. By constructing a functional formulation of the inner product on tangent space, the CCM technique is applied to develop a local ancillary controller to guarantee the actual trajectory being contained within a robust invariant tube. In addition, to alleviate the burden of computing geodesic in the design of CCM technique, we propose a direct method using the neural network to search for the minimal geodesic online. By employing the sequential quadratic programming algorithm, this method improves the efficiency of online calculation with high accuracy. Finally, numerical simulation is employed to validate the effectiveness of the proposed method.

关键词： control contraction metric geodesic problem robust model predictive control uncertain time‐ delay systems

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robust model predictive control of wind turbines based on Bayesian parameter self-optimization

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Tang, Minan Wang, Wenjuan Yan, Yaguang Zhang, Yaqi An, Bo Lanzhou Jiaotong Univ Sch New Energy & Power Engn Lanzhou Peoples R China Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou Peoples R China

This paper studies the effect of different turbulent wind speeds on the operation of wind turbines. The proportion of wind power in the field of new energy generation has increased massively and has gained wide application and attention. However, the smooth operation and the stability of the output power of the wind power generation system are susceptible to wind speed fluctuations. To tackle this problem, this paper takes a 5 MW horizontal axis wind turbine as the research object that proposes a parameter adaptive robust control method to achieve self-optimization of controller parameters by means of Bayesian optimization. The 5 MW wind turbine model is utilized to verify the feasibility of the algorithm by combining the wind speed types commonly found in a high-altitude region in northwestern. The simulation results validate the effectiveness of the proposed scheme. The outcomes demonstrate that Bayesian optimization can significantly decrease the effects of wind speed instability. The output power increases by 1.9% on average at low wind speed and stabilizes on 5 MW at high wind speed. Therefore, the stable controller for wind power output is the robust model predictive controller with parameter improvement.

关键词： Bayesian optimization parameter self-optimization robust model predictive control 5 MW wind turbine high altitude areas in northwestern China

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The Interval Dynamic model of the DAB Converter is Used for robust model predictive control

The Interval Dynamic Model of the DAB Converter is Used for ...

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International Conference on Wireless Power Transfer (ICWPT)

作者： Wang, Jiarui Wang, Xiaolan Hao, Rui Li, Zhaohui Wang, Lixin Yue, Meng Lanzhou Univ Technol Lanzhou 730000 Peoples R China

ISBN: (纸本)9789819708673;9789819708659;9789819708642

This paper focuses on the robustness issue caused by load resistance variations in the dual active bridge DC-DC (DAB) converter. It proposes a robust model predictive control algorithm and establishes an interval dynamic model. The algorithm consists of two parts: offline and online. In the offline part, the system parameters are treated as intervals instead of single values, and the corresponding interval dynamic model is established. Then, linear matrix inequalities (LMI) are used to solve the feedback control gain sequences. In the online part, the actual control law of the system is determined based on the position of the state variables in the polyhedral invariant set. Simulation results demonstrate that the designed controllers can maintain stable voltage output and good dynamic characteristics of the DAB converter under input voltage disturbances and load resistance variations. This ensures a certain level of robustness. Simulation results demonstrate the ability of the designed controllers to maintain stable voltage output and good dynamic characteristics of the DAB converter under input voltage disturbances and load resistance variations, ensuring a certain level of robustness.

关键词： Dual Active Bridge DC-DC Converter robust model predictive control Interval Dynamic model Linear Matrix Inequality

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Fast System Level Synthesis: robust model predictive control using Riccati Recursions 8

Fast System Level Synthesis: Robust Model Predictive Control...

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8th IFAC Conference on Nonlinear model predictive control (NMPC)

作者： Leeman, Antoine P. Kohler, Johannes Messerer, Florian Lahr, Amon Diehl, Moritz Zeilinger, Melanie N. Swiss Fed Inst Technol Inst Dynam Syst & Control CH-8053 Zurich Switzerland Univ Freiburg Dept Microsyst Engn IMTEK D-79110 Freiburg Germany Univ Freiburg Dept Math D-79104 Freiburg Germany

System level synthesis enables improved robust MPC formulations by allowing for joint optimization of nominal trajectory and controller. This paper introduces a tailored algorithm for solving the corresponding disturbance feedback optimization problem for linear time-varying systems. The proposed algorithm iterates between optimizing the controller and the nominal trajectory while converging q-linearly to an optimal solution. We show that the controller optimization can be solved through Riccati recursions leading to a horizon-length, state, and input scalability of O(N-2(n(x)(3) + n(u)(3))) for each iterate. On a numerical example, the proposed algorithm exhibits computational speedups by a factor of up to 10(3) compared to general-purpose commercial solvers. Copyright (C) 2024 The Authors.

关键词： Optimization and model predictive control robust model predictive control Real-Time Implementation of model predictive control

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Aperiodic event-triggered robust model predictive control for linear parameter-varying system with round-robin protocol

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INTERNATIONAL JOURNAL OF robust AND NONLINEAR control 2022年第1期32卷 227-242页

作者： Wang, Jun Ding, Baocang Wang, Ping Chongqing Univ Posts & Telecommun Coll Automat Chongqing 400065 Peoples R China

This article investigates the robust model predictive control (MPC) problem for networked control systems represented by the linear parameter-varying model, in which an event-triggered strategy and the round-robin (RR) protocol scheduling locate at the sensor-to-controller and controller-to-actuator channels, respectively. By considering the problems of system state immeasurable and communication burden in engineering application, an output feedback controller that combines the aperiodic event-triggered strategy is applied, where the triggering condition is designed in a time-varying fashion. In addition, in order to avoid unexpected data collisions, the RR protocol is utilized to schedule a shared network and guarantee the efficiency of the control system. The controller parameters are obtained by solving an online convex robust MPC optimization problem, and the feasibility of the optimization problem and closed-loop stability are also addressed. The effectiveness of the proposed theoretical results is illustrated by a numerical simulation example.

关键词： aperiodic event-triggered strategy feasibility linear parameter-varying model robust model predictive control round-robin protocol

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Trajectory Tracking for Autonomous Vehicles Using robust model predictive control 17

Trajectory Tracking for Autonomous Vehicles Using Robust Mod...

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17th IFAC Symposium on control of Transportation Systems (CTS)

作者： Shen, Dan Chen, Yaobin Li, Lingxi Hu, Jianghai Aptiv Corp Troy MI 48098 USA Purdue Univ Indianapolis IN 46202 USA Purdue Univ W Lafayette IN 47907 USA

This paper proposes a novel trajectory tracking control method using a robust model predictive control (RMPC) design technique based on matrix inequality and the theoretical approach of the hybrid & switched system. Firstly, considering bounded model uncertainties and norm-bounded external disturbances, the system states and control matrices are modified. In addition, vehicle time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. The numerical simulation results demonstrated that the proposed robust LPV MPC can effectively track vehicle lateral dynamics on both straight and curved roads with varying longitudinal speeds. Copyright (c) 2024 The Authors.

关键词： Trajectory tracking control linear parameter varying robust model predictive control

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Data-based robust model predictive control for wastewater treatment process

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JOURNAL OF PROCESS control 2022年第0期118卷 115-125页

作者： Han, Hong-Gui Wang, Chen-Yang Sun, Hao-Yuan Qiao, Jun-Fei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Artificial Intelligence Inst Beijing 100124 Peoples R China Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China

Data-driven methods are widely used in wastewater treatment processes (WWTPs). However, disturbances are not fully considered during the control process, which may affect the stable operation of WWTPs. To address this issue, a data-driven robust model predictive control (DRMPC) method is proposed for WWTPs with disturbances. First, an interval type-2 fuzzy neural network (IT2FNN), which has high robust performance to disturbances, is used to identify dynamic behavior of WWTPs. Second, based on IT2FNN, a robust stabilizing controller is developed to weaken the influence of disturbances on control performance and stability. Third, the proposed controller ensures constraint satisfaction and input-to-state stability (ISS). Finally, the feasibility of DRMPC strategy is verified on the benchmark simulation model No. 1 (BSM1). The experimental studies indicate that the proposed DRMPC is capable of running stably and tracking accurately. (C) 2022 Published by Elsevier Ltd.

关键词： Data-driven A learning method robust model predictive control Wastewater treatment process

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A robust model predictive control Based Frequency Regulation Approach for Wind-Storage Joint System

A Robust Model Predictive Control Based Frequency Regulation...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Tang, Zao Jia, Wenjie Liu, Jia Tang, Yi Zeng, Pingliang Southeast Univ Liyang Res Inst Liyang 213300 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Hangzhou Dianzi Univ Dept Automat Hangzhou 310038 Peoples R China Sichuan Univ Sichuan Prov Key Lab Smart Grid Chengdu 610065 Sichuan Provinc Peoples R China

ISBN: (纸本)9798350339345

Compared to wind power participating in grid frequency regulation independently, a wind-storage joint system has a better frequency regulation performance. Considering the high uncertainty of wind power generation due to environmental factors, this article proposes an optimization strategy for the wind-storage joint system to participate in frequency regulation based on robust model predictive control. First, based on the principle of frequency regulation, the response models of element in windstorage joint systems are established. Secondly, aiming at minimizing the deviation cost of the system's frequency regulation power in the worst wind power output scenario, a two-layer objective function shaped like min-max is constructed, and the strong duality theory is used to transform it into a single-layer robust optimization model. Then, combined with the model predictive control idea, a robust model predictive control (RMPC) based frequency regulation model is formed. Finally, by comparing the effect difference between the model prediction control method in the deterministic scenario and the proposed method, the superiority of the proposed method in coping with the frequency regulation ability of wind power uncertainty fluctuation is proved.

关键词： frequency regulation wind-storage joint system uncertainty robust model predictive control duality theory

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