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Cooperative Ecological adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on Approximate dynamic programming

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ieee TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3132-3145页

作者： Li, Jie Liu, Yonggang Fotouhi, Abbas Wang, Xiangyu Chen, Zheng Zhang, Yuanjian Li, Liang Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Cranfield Univ Adv Vehicle Engn Ctr Sch Aerosp Transport & Mfg Cranfield MK43 0AL England Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650500 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England

Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. In addition, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation.

关键词： Approximate dynamic programming cooperative adaptive cruise control eco-driving plug-in hybrid electric vehicle velocity optimization

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adaptive Optimal Control for Vehicle Platoons With Uncertain Communication Topology

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ieee TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 12500-12514页

作者： Wen, Shixi Guo, Ge Zhao, Yuan Gao, Zhenyu Dalian Univ Sch Informat & Engn Dalian 116622 Peoples R China Dalian Univ Minist Educ Key Lab Adv Design & Intelligent Comp Dalian 116622 Peoples R China Northeastern Univ State Key Lab Synthet Automat Ind Proc Shenyang 110004 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China

This study proposes the reinforcement learning adaptive optimal (RLAO) control scheme for vehicle platoons subject to unmodeled vehicle dynamics, unknown external disturbances and uncertain inter-vehicle communication topology. By combining the actor-critic RL algorithm with adaptive backstepping technique, a novel RLAO control framework is established for each follower to stabilize the tracking errors and optimize the control performance. The proposed RLAO controller does not depend on the exact topological matrix and vehicle dynamics, as only the input and output data from the tracking error dynamics are involved in updating the control input. Based on the optimal control and dynamic programming, a useful algorithm is proposed to implement the RLAO controller such that all the error signals of the platoon control system are ultimately uniformly bounded. Particularly, the tracking errors can converge to a small neighborhood of zero by setting appropriate parameters. Furthermore, the platoon disturbance string stability is guaranteed. A comparative analysis based on simulation examples is discussed to illustrate the effectiveness of the proposed RLAO controller.

关键词： Vehicle dynamics Stability analysis Topology Control systems Backstepping Switches Roads Actor-critic neural network backstepping control disturbance string stability reinforcement learning uncertain inter-vehicle communication topology vehicles platoon

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Echo State Network-Based Robust Tracking Control for Unknown Constrained Nonlinear Systems by Using Integral reinforcement learning

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ieee ACCESS 2024年 12卷 15133-15144页

作者： Liu, Chong Li, Yalun Duan, Zhongxing Chu, Zhousheng Ma, Zongfang Xian Univ Architecture & Technol Coll Informat & Control Engn Xian 710055 Shaanxi Peoples R China

It is necessary to consider the robustness in the tracking problem, which can effectively suppress the external disturbance to ensure the tracking performance. Different from previous tracking control methods, considering the robustness, completely unknown nonlinear system dynamics and constrained controller, we propose a data-based echo state network (ESN) approximated algorithm for a class of robust tracking problems. First, the robust tracking control problem (RTCP) is transformed into the optimal control problem of the according nominal system by designing a elaborate value function. To obtain the optimal control policy, we have to solve a Hamilton-Jacobi-Bellman equation (HJBE) about the augmented nominal system. It is well-known that modelling the accurate dynamics for the practical engineering applications is usually difficult, so the model-free integral reinforcement learning (IRL) algorithm is used to learn the optimal control policy and performance function simultaneously by only using systems data. In this IRL algorithm, a reservoir computing based ESN is used to approximate the performance function and control input. Contrast to other neural networks, ESN need not consider the choice of activation function, which can greatly reduce the difficulty and effort of neural network structure design. The output weights of the ESNs are iteratively updated towards the optimal ones by using least square algorithm and the pre-collected off-line system data. Then, using the converged output weights and ESNs, the tracking control input can be derived without knowing any system dynamic information. Finally, we demonstrate that the given system can be controlled to track the desired trajectory well under the proposed method by using two simulation examples.

关键词： adaptive dynamic programming (ADP) echo state network (ESN) integral reinforcement learning (IRL) robust control tracking control

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Power management optimisation for hybrid electric systems using reinforcement learning and adaptive dynamic programming

Power management optimisation for hybrid electric systems us...

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American Control Conference (ACC)

作者： Sanusi, Ibrahim Mills, Andrew Konstantopoulos, George Dodd, Tony Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)9781538679265

This paper presents an online learning scheme based on reinforcement learning and adaptive dynamic programming for the power management of hybrid electric systems. Current methods for power management are conservative and unable to fully account for variations in the system due to changes in the health and operational conditions. These conservative schemes result in less efficient use of available power sources, increasing the overall system costs and heightening the risk of failure due to the variations. The proposed scheme is able to compensate for modelling uncertainties and the gradual system variations by adapting its performance function using the observed system measurements as reinforcement signals. The reinforcement signals are nonlinear and consequently neural networks are employed in the implementation of the scheme. Simulation results for the power management of an autonomous hybrid system show improved system performance using the proposed scheme as compared with a conventional offline dynamic programming approach.

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Kernel-based least squares policy iteration for reinforcement learning

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ieee TRANSACTIONS ON NEURAL NETWORKS 2007年第4期18卷 973-992页

作者： Xu, Xin Hu, Dewen Lu, Xicheng Natl Univ Def Technol Coll Mechatron & Automat Inst Automat Changsha 410073 Peoples R China Natl Univ Def Technol Coll Mechatron & Automat Dept Automat Control Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating a

关键词： approximate dynamic programming kernel methods least squares Markov decision problems (MDPs) reinforcement learning (RL)

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Optimal control applied to Wheeled Mobile Vehicles

Optimal control applied to Wheeled Mobile Vehicles

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ieee International symposium on Intelligent Signal Processing

作者： Gomez, M. Martinez, T. Sanchez, S. Meziat, D. Univ Alcala Escuela Politecn Super Dept Automat Alcala De Henares Spain Univ Alicante Escuela Politecn Super Ingn Sistemas Teoria Sefial Dept Fis Alicante Spain

ISBN: (纸本)9781424408290

The goal of the work described in this paper is to develop a particular optimal control technique based on a Cell. Mapping technique in combination with the Q-learning reinforcement learning method to control wheeled mobile vehicles. This approach manages 4 state variables due to a dynamic model is performed instead of a kinematics model which can be done with less variables. This new solution can be applied to non-linear continuous systems where reinforcement learning methods have multiple constraints. Emphasis is given to the new combination of techniques, which applied to optimal control problems produce satisfactory results. The proposed algorithm is very robust to any change involved In the vehicle parameters because the vehicle model is estimated in real time from received experience.

关键词： Cell-Mapping dynamic programming optimal control principle of optimality Q-learning reinforcement learning

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Applications of Linear adaptive dynamic programming (ADP) to a Non-linear Four-bar Mechanism 11

Applications of Linear Adaptive Dynamic Programming (ADP) to...

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11th International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Sveen, Emil Muhlbradt Zhou, Jing Univ Agder Fac Sci & Engn Kristiansand Norway

ISBN: (纸本)9798350315684

This paper investigates the feasibility of using learning based adaptive dynamic programming (ADP) for linear time-invariant systems to control the position of a highly non-linear four-bar system. An off-line off-policy controller iteration approach, inspired by reinforcement learning, is used to find a near-optimal controller for the four-bar crank motor. The results show that convergence of a near-optimal linear control policy is possible under the chosen exploration strategy, and the paper further exploit the non-linearity of the four-bar to investigate the limits of the exploration strategy parameters to shed light on the non-triviality of selecting a suitable exploration strategy in ADP. The efficiency of the ADP approach is demonstrated, as just 1.4 seconds of simulation time is required to convergence to a near-optimal ADP controller.

关键词： learning based control non-linear control adaptive dynamic programming

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Model-Free Value Iteration Solution for dynamic Graphical Games 23

Model-Free Value Iteration Solution for Dynamic Graphical Ga...

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ieee International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

作者： Abouheaf, Mohammed Gueaieb, Wail Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781538646182

The dynamic graphical game is a special class of games where agents interact within a communication graph. This paper introduces an online model-free adaptive learning solution for dynamic graphical games. A reinforcement learning is applied in the form solutions to a set of modified coupled Bellman equations. The technique is implemented in a distributed fashion using the local neighborhood information without having a priori knowledge about the agents' dynamics. This is accomplished by means of adaptive critics, where a multi-layer perceptron neural network is applied to approximate the online solution. To this end, a novel coupled Riccati equation is developed for the graphical game. The validity of the proposed online adaptive learning solution is tested using a graphical example, where follower agents learn to synchronize their behavior to follow a leader.

关键词： Mathematical model Games dynamic programming Vehicle dynamics Synchronization Heuristic algorithms learning (artificial intelligence)"

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State-Dependent adaptive dynamic Programing for a Class of Continuous-Time Nonlinear Systems 3

State-Dependent Adaptive Dynamic Programing for a Class of C...

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3rd International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Batmani, Yazdan Davoodi, Mohammadreza Meskin, Nader Univ Kurdistan Dept Elect Engn Sanandaj Iran Qatar Univ Dept Elect Engn Doha Qatar

ISBN: (纸本)9781509021888

The state-dependent Riccati equation (SDRE) technique can be used to solve optimal control problems for a wide class of nonlinear dynamical systems. In this method, instead of solving a complicated Hamilton-Jacobi-Bellman (HJB) equation, a state-dependent Riccati equation is solved which leads to a suboptimal control law. However, a priori model of the system must be available to apply this technique to the optimal control problem. In this paper, to solve the SDRE without using a priori model of the system, a direct adaptive suboptimal algorithm is proposed. The algorithm, named state-dependent Riccati equation adaptive dynamic programming (SDRE-ADP), is based on a reinforcement learning approach which can be implemented in an online fashion. Like the SDRE technique, the proposed SDRE-ADP can locally asymptotically stabilize the closed-loop system provided that some conditions are satisfied. Application of the proposed algorithm to an autonomous unmanned underwater vehicle (AUV) and a numerical example shows that it can be effectively applied for nonlinear systems.

关键词： adaptive dynamic programming Nonlinear optimal control reinforcement learning SDRE technique

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A learning Model for Personalized adaptive Cruise Control 28

A Learning Model for Personalized Adaptive Cruise Control

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28th ieee Intelligent Vehicles symposium (IV)

作者： Chen, Xin Zhai, Yong Lu, Chao Gong, Jianwei Wang, Gang Beijing Inst Technol Intelligent Vehicle Res Ctr Beijing 100081 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9781509048045

This paper develops a learning model for personalized adaptive cruise control that can learn from human demonstration online and mimic a human driver's driving strategies in the dynamic traffic environment. Under the framework of the proposed model, reinforcement learning is used to capture the human-desired driving strategy, and the proportion-integration-differentiation controller is adopted to convert the learning strategy to low-level control commands. The performance of the learning model is tested in the simulation environment built in a driving simulator using PreScan. Experimental results show that the learning model can duplicate human driving strategies with acceptable errors. Moreover, compared with the traditional adaptive cruise control, the proposed model can provide better driving comfort and smoothness in the dynamic situation.

关键词： Vehicles Adaptation models learning (artificial intelligence) Vehicle dynamics Acceleration Cruise control Data models

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