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Data-Learning Game Output Regulation Approach for Human-Machine Cooperative Driving Toward Varied Drivers and Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第12期25卷 20190-20202页

作者： Guo, Hongyan Shi, Wanqing Guo, Jingzheng Liu, Jun Cao, Dongpu Chen, Hong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130012 Peoples R China Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

For personalized human-machine cooperative (HMC) control, traditional model-driven approaches, which rely on predefined driver-vehicle-road (DVR) models, often struggle to adapt to individual driver differences. To address this, a data-learning shared control strategy based on game output regulation and adaptive dynamic programming (ADP) is presented. Firstly, considering the differences in driver's characteristics, vehicle-road dynamics and human-machine interaction, an uncertain DVR system is established. Subsequently, robust output regulation (ROR) is utilized to handle road curvature perturbations and ensure closed-loop system stability. Subsequently, a dynamic game framework between the front-wheel steering system (AFS) and the active rear-wheel steering system (ARS) is further developed to ensure both vehicle stability and path-tracking accuracy in complex environments. Finally, the AFS-ARS optimal control strategies are iteratively learned and updated by ADP, using online DVR system data, without requiring prior knowledge of specific drivers or vehicles. Through driver-in-the-loop experiments, it is demonstrated that the presented method exhibits good adaptability to different drivers.

关键词： Human-machine cooperative driving personalized drivers adaptive dynamic programming game output regulation

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Robust dynamic event-triggered control of saturated nonlinear systems using reinforcement learning

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第4期34卷 2659-2674页

作者： Zheng, Wenqian Yang, Xiong Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

We develop a robust dynamic event-triggered control (ETC) law for asymmetrically input-saturated nonlinear systems having matched disturbances. Initially, by constructing a novel cost function for the associated nominal system, we transform the constrained robust stabilization problem into an unconstrained optimal control problem. Then, we propose a dynamic event-triggering rule for improving the performance in reducing the computational burden. Based on such a dynamic triggering rule, we present the event-triggered Hamilton-Jacobi-Bellman equation (ET-HJBE). To solve the ET-HJBE, we employ a critic approximator with its parameters being updated in the reinforcement learning framework. After that, we apply Lyapunov's direct method to prove uniform ultimate boundedness of the closed-loop system and the critic approximator's parameter estimation error. Finally, we provide two nonlinear plants to validate the present dynamic ETC law.

关键词： adaptive dynamic programming asymmetric saturation dynamic event-triggered control reinforcement learning robust control

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Observer-based optimal fault-tolerant tracking control for input-constrained interconnected nonlinear systems with mismatched disturbances

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第6期45卷 2572-2595页

作者： Liu, Shihui Xu, Ning Zhao, Ning Zhang, Liang Bohai Univ Coll Control Sci & Engn Jinzhou Liaoning Peoples R China Bohai Univ Coll Informat Sci & Technol Jinzhou 121013 Liaoning Peoples R China

This paper investigates an observer-based optimal fault-tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances via adaptive dynamic programming (ADP). Firstly, an augmented system consisting of tracking error and the reference trajectory is constructed, and then the original optimal tracking control problem is transformed into the optimal regulation control problem of the augmented system. An integral sliding-mode-based optimal fault-tolerant control scheme is developed to eliminate the effect of actuator faults and guarantee the optimal control performance. Furthermore, a neural network-based observer is designed to identify the completely unknown system dynamics based on the input-output data, thereby relaxing the restriction on system dynamics. Subsequently, the modified Hamilton-Jacobi-Bellman equations are solved by using the ADP algorithm under a critic network framework. According to the Lyapunov approach, all signals in the closed-loop augmented system are uniformly ultimately bounded. Finally, the effectiveness of the developed control scheme is demonstrated via simulation results. This paper investigates an optimal fault-tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances. An observer-based optimal fault-tolerant control scheme is developed under a critic network framework by combining integral sliding-mode control technique. According to the Lyapunov approach, all signals in the closed-loop augmented system are uniformly ultimately bounded. The effectiveness of the developed control scheme is demonstrated via simulation results. image

关键词： adaptive dynamic programming decentralized tracking control input constraints integral sliding-mode fault-tolerant control interconnected nonlinear systems

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Evolution-guided value iteration for optimal tracking control

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NEUROCOMPUTING 2024年 593卷

作者： Huang, Haiming Wang, Ding Zhao, Mingming Hu, Qinna Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

In this article, an evolution-guided value iteration (EGVI) algorithm is established to address optimal tracking problems for nonlinear nonaffine systems. Conventional adaptive dynamic programming algorithms rely on gradient information to improve the policy, which adheres to the first order necessity condition. Nonetheless, these methods encounter limitations when gradient information is intricate or system dynamics lack differentiability. In response to this challenge, evolutionary computation is leveraged by EGVI to search for the optimal policy without requiring an action network. The competition within the policy population serves as the driving force for policy improvement. Therefore, EGVI can effectively handle complex and non-differentiable systems. Additionally, this innovative method has the potential to enhance exploration efficiency and bolster the robustness of algorithms due to its population-based characteristics. Furthermore, the convergence of the algorithm and the stability of the policy are investigated based on the EGVI framework. Finally, the effectiveness of the established method is comprehensively demonstrated through two simulation experiments.

关键词： adaptive critic designs adaptive dynamic programming Evolutionary computation Intelligent control Optimal tracking Reinforcement learning

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Adjustable iterative Q-learning for advanced neural tracking control with stability guarantee

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NEUROCOMPUTING 2024年 584卷

作者： Wang, Yuan Wang, Ding Zhao, Mingming Liu, Ao Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Key Lab Computat Intelligence & Intelligent Syst Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

In this article, an accelerated Q -learning algorithm with evolving control is established to solve the optimal tracking control problem. First, an accelerated Q -learning scheme is constructed with an advanced Q -function. By utilizing the advanced Q -function, calculating of the feedforward control input can be avoided and the terminal tracking error can be eliminated. Then, by introducing the relaxation factor, the convergence rate of the iterative Q -function sequence is accelerated significantly, which is a potential way to diminish the computational burden. Furthermore, the convergence, positive definiteness, and stability conditions of the accelerated Q -learning algorithm are analyzed with some preconditions of the relaxation factor. Thus, the developed algorithm can achieve evolving control. Finally, the fantastic performance of the developed algorithm with critic network implementation is verified through two simulation examples.

关键词： adaptive dynamic programming Accelerated convergence rate Nonlinear systems Q-learning Stability analysis Neural tracking control

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Observer-based event-triggered recursive optimal tracking control for a class of strict-feedback nonlinear systems☆

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ISA TRANSACTIONS 2024年 145卷 148-162页

作者： Zhang, Wenguang Yan, Jin Xuzhou Univ Technol Sch Informat Engn Lishui Rd 2 Xuzhou Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Xiaolingwei 200 Nanjing Peoples R China

In this paper, an innovative event -triggered optimal tracking control algorithm is proposed for input saturated strict -feedback nonlinear systems with unknown dynamics. In order to reduce the requirement of configuring a complete suit of sensors and enhance the reliability of the controlled system, a neural networks (NNs) based adaptive state observer is developed firstly to reconstruct the system states. Subsequently, based on the state estimation information, a hybrid -triggered feedforward controller is designed to transform the original tracking control problem into an equivalent regulation issue, which is then solved by developing an event -triggered optimal controller. Therefore, the final controller consists of a hybrid -triggered feedforward controller and an event -triggered optimal controller. In order to make the actual input signals of the two controllers be updated simultaneously, a synchronization -oriented triggering rule is established by using multiple triggering errors. By virtue of this unique framework, the proposed control scheme can not only minimize the predefined cost function, but also greatly reduce the data transmission. What is more, the convergence properties of the proposed control strategy are achieved by using Lyapunov theory. It is important to note that unlike the widely adopted observer -controller framework, where the separation principle holds for the design of the state observer, there is a considerable coupling relationship between the error dynamics of the state observer and the event -triggered optimal controller in this paper. The distinguishing feature of the proposed method is its ability to ensure a satisfactory level of precision in both state estimation and tracking control, even in the presence of control saturation issues. At last, the proposed control strategy is applied to the tracking control problem of a high -order robot system and marine surface vehicle to demonstrate its effectiveness.

关键词： Strict-feedback Event-triggered control Output feedback control Jump of error surfaces adaptive dynamic programming

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A new Q-function structure for model-free adaptive optimal tracking control with asymmetric constrained inputs

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INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2024年第5期38卷 1561-1578页

作者： Zhao, Mingming Wang, Ding Li, Menghua Gao, Ning Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

This article aims to design a model-free adaptive tracking controller for discrete-time nonlinear systems with unknown dynamics and asymmetric control constraints. First, a new Q-function structure is designed by introducing the control input into the tracking error of the next moment, in order to eliminate the final tracking error, avoid the steady control, and ignore the discount factor. Second, via system transformation, a general performance index is developed to overcome the challenge caused by asymmetric constraints of implicit control inputs. By this operation, the constrained tracking problem is converted to an unconstrained optimal tracking problem without the traditional nonquadratic performance function that is only applicable to explicit control inputs. Then, a value-iteration-based Q-learning (VIQL) algorithm is derived to seek the optimal Q-function and the optimal control policy by using offline data rather than the mathematical model. Next, the convergence, monotonicity, and stability properties of VIQL are investigated to demonstrate that the iterative Q-function sequence can converge to the optimal Q-function under ideal conditions. To realize the VIQL algorithm, the critic neural network is employed to approximate the Q-function. Finally, simulation results and comparative experiments are conducted to demonstrate the validity and effectiveness of the present VIQL scheme.

关键词： adaptive dynamic programming asymmetric control constraints model-free adaptive optimal tracking value-iteration-based Q-learning

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Data-Driven Cooperative Differential Game Based Energy Management Strategy for Hybrid Electric Propulsion System of a Flying Car

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 14770-14784页

作者： Ruan, Shumin Ma, Yue Wei, Zhengchao Zhang, Chongbing Xiang, Changle Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Natl Key Lab Vehicular Transmiss Beijing 100081 Peoples R China

The presence of multiple generator units (GUs) in the hybrid electric propulsion system (HEPS) of flying cars poses higher requirements for the design of energy management strategy (EMS) since the decision made by one GU impacts the state and decisions of others due to the coupling electrical dynamics of the system. In this paper, a data-driven cooperative differential game (CDG) based EMS is proposed to improve the performance of the fuel consumption and exhaust gas temperature (EGT) of those GUs as well as the stability of the state of charge (SOC) of the battery through coordination and cooperation. The energy management problem is first formulated as a general two-player differential game. To improve the computational efficiency as well as the control performance, a novel neural network-based adaptive dynamic programming (ADP) algorithm is proposed to approximate the Nash equilibrium (NE) and Pareto solution (PS) of the non-cooperative differential game (NCDG) and CDG During real-time application, and a comparison mechanism is designed so that the solution with a smaller cost is applied to the system to further improve performance. The simulation results indicate that the proposed CDG-based EMS can not only reduce the equivalent fuel consumption by 2.67% and 6.22% compared with that of NCDG and rule-based EMS, but also obtain a better overall and individual performance of the two GUs simultaneously, demonstrating the effectiveness of the proposed approach in reducing the fuel consumption, EGT as well as maintaining the state of charge (SOC) of the battery.

关键词： Energy management Automobiles Real-time systems Engines Fuels Differential games Batteries Energy management strategy differential game adaptive dynamic programming hybrid electric propulsion system flying car

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adaptive Event-Triggered Optimal Tracking Control for Wheeled Mobile Robots Considering Force-Velocity Hybrid Constraints

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Ren, Tao Li, Shu Liu, Yan-Jun Wan, Feng Liu, Lei Sun, Cong Liaoning Univ Technol Prov Key Lab Intelligent Control Theory & Applicat Jinzhou 121001 Peoples R China Univ Macau Inst Collaborat Innovat Fac Sci & Technol Macau Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110000 Peoples R China

In the soft deformable terrain environment, the running state of the wheeled mobile robot is easily affected by the complex wheel-ground interaction, which limits its running state variables and input torque. In this paper, the tracking control of wheeled mobile robot under soft deformable terrain is studied, and a dynamic event trigger mechanism is proposed. Based on the proposed trigger strategy, an adaptive event trigger optimal tracking control algorithm for wheeled mobile robot system with nonlinear constraints is designed. By analyzing the nonlinear constraint problem faced by the dynamic model of wheeled mobile robot considering skidding and slipping, the dynamic model of wheeled mobile robot in soft deformable terrain environment with force-speed mixed constraints is constructed. Combining the force-speed constraint and the state error event-triggered idea, a dynamic event-triggered mechanism containing constraint information is designed, and Zeno behavior is avoided. An adaptive event-triggered optimal controller is constructed by combining adaptive dynamic programming algorithm and policy iteration algorithm. To make the wheeled mobile robot complete the tracking control. Finally, it is verified by simulation.

关键词： Mobile robots Wheels Optimal control Robot kinematics Heuristic algorithms Event detection dynamics Torque Cost function Mathematical models Soft deformable terrain dynamic event trigger force-velocity hybrid constraints adaptive dynamic programming neural network

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Decentralized approximated optimal control for modular robot manipulations with physical human-robot interaction: a cooperative game-based strategy

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NONLINEAR dynamicS 2024年第9期112卷 7145-7158页

作者： An, Tianjiao Zhu, Xinye Ma, Bing Zhu, Mingchao Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys CAS Key Lab Onorbit Mfg & Integrat Space Opt Syst Changchun 130033 Peoples R China

Major challenges of controlling physical human-robot interaction (pHRI)-oriented modular robot manipulator (MRM) include performance optimization and solving the coupling effect between the human and the robot as well as MRM subsystems. In this paper, a cooperative game-based decentralized optimal control approach is developed for MRMs with pHRI. The joint torque feedback (JTF) technique is utilized to form the MRM dynamic model, then, the major objective of optimal control with pHRI is transformed into approximating Pareto equilibrium by adopting cooperative game governed between the human and the MRM that are regarded as players with different tasks and optimization criteria in interaction process. On the basis of adaptive dynamic programming (ADP) algorithm, the decentralized approximate optimal control strategy with pHRI is developed by a critic neural network (NN)-based cooperative game manner for solving the coupled Hamilton-Jacobian-Bellman (HJB) equation. The position tracking error under pHRI is verified to be ultimately uniformly bounded (UUB). Experiment results have been presented, which exhibit the superiority of the proposed method.

关键词： adaptive dynamic programming Modular robot manipulators Cooperative game Decentralized control Physical human-robot interaction

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