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43rd Chinese Control Conference (CCC)

作者： Zhou, Yinglu Li, Yinya Sheng, Andong Qi, Guoqing Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

This paper investigates a single-pursuer single-evader (SPSE) differential game. Unlike traditional treatments, both the dynamics of the pursuer and the evader are nonlinear which makes the linear quadratic regulation can not be applicable. To solve this problem, this paper constructs the critic neural network (NN) framework to approximate the solution of the HamiltonJacobi-Isaacs (HJI) equations through the adaptive dynamic programming (ADP) method. The interception condition, i.e., the stability of the system is also analyzed via the Lyapunov theorem. A numerical example is presented to illustrate that and the results show that the critic NN weights estimation error and the system are uniformly ultimately bounded (UUB), i.e., the pursuer can successfully intercept the evader.

关键词： Single-pursuer Single-evader Differential Game adaptive dynamic programming Stackelberg Equilibrium

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adaptive dynamic programming and Data-Driven Cooperative Optimal Output Regulation with adaptive Observers 61

Adaptive Dynamic Programming and Data-Driven Cooperative Opt...

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IEEE 61st Conference on Decision and Control (CDC)

作者： Qasem, Omar Jebari, Khalid Gao, Weinan Florida Inst Tech nology Dept Mech & Civil Engn Coll Engn & Sci Melbourne FL 32901 USA Florida Inst Technol Dept Aerosp Engn Coll Engn & Sci Melbourne FL 32901 USA

ISBN: (纸本)9781665467612

In this paper, a novel adaptive optimal control strategy is proposed to achieve the cooperative optimal output regulation of continuous-time linear multi-agent systems based on adaptive dynamic programming (ADP). The proposed method is different from those in the existing literature of ADP and cooperative output regulation in the sense that the knowledge of the exosystem dynamics is not required in the design of the exostate observers for those agents with no direct access to the exosystem. Moreover, an optimal control policy is obtained without the prior knowledge of the modeling information of any agent while achieving the cooperative output regulation. Instead, we use the state/input information along the trajectories of the underlying dynamical systems and the estimated exostates to learn the optimal control policy. Simulation results show the efficacy of the proposed algorithm, where both estimation errors of exosystem matrix and exostates, and the tracking errors converge to zero in an optimal sense, which solves the cooperative optimal output regulation problem.

关键词： Optimal control reinforcement learning adaptive dynamic programming cooperative optimal control cooperative optimal output regulation

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adaptive dynamic programming based Control Scheme for Uncertain Two-Wheel Robots

Adaptive Dynamic Programming based Control Scheme for Uncert...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Thien Van Nguyen Hai Xuan Le Hoang Viet Tran Duc Anh Nguyen Minh Ngoc Nguyen Linh Nguyen Hanoi Univ Ind Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam Federat Univ Australia Melbourne Vic Australia

ISBN: (纸本)9781665431989

The paper addresses the problem of effectively controlling a two-wheel robot given its inherent non-linearity and parameter uncertainties. In order to deal with the unknown and uncertain dynamics of the robot, it is proposed to employ the adaptive dynamic programming, a reinforcement learning based technique, to develop an optimal control law. It is interesting that the proposed algorithm does not require kinematic parameters while finding the optimal state controller is guaranteed. Moreover, convergence of the optimal control scheme is theoretically proved. The proposed approach was implemented in a synthetic two-wheel robot where the obtained results demonstrate its effectiveness.

关键词： Reinforcement learning two-wheel robot adaptive control adaptive dynamic programming

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adaptive dynamic programming for kinematic control of 3 interconnected wheeled mobile robots 32

Adaptive dynamic programming for kinematic control of 3 inte...

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32nd International Conference on Electrical Engineering (ICEE)

作者： Ghasemzadeh, Aliakbar Amjadifard, Roya Keymasi-Khalaji, Ali Kharazmi Univ Dept Elect & Comp Engn Tehran Iran Kharazmi Univ Dept Mech Engn Tehran Iran

ISBN: (纸本)9798350376357;9798350376340

This paper proposes an adaptive dynamic programming (ADP) control approach for kinematic tracking of 3 interconnected wheeled mobile robots (IWMR). The kinematic model of the interconnected mobile robot system is derived. An affine form error system is then defined to facilitate the application of the ADP control method, which renders it a partially unknown control method. In particular, an actor-critic reinforcement learning (RL) neural network (NN) structure is employed, in which the critic NN approximates the value function. An adaptive learning method based on Hamiltonian error minimizes a predefined long-term cost function for updating the critic NN weights to guarantee their convergence to the ideal weights. Actors use these weights to provide control policies. This ADP control scheme is suitable for partially unknown affine systems. Simulation results demonstrate that the proposed ADP controller achieves accurate trajectory tracking for mobile robots. This significantly outperforms previous methods because the tracking error and control input are substantially reduced compared to other techniques. This study highlights the potential of ADP methods for complex nonlinear control problems in interconnected mobile robotic systems.

关键词： 3 IWMR path tracking adaptive dynamic programming actor-critic neural networks

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adaptive dynamic programming-based Fault Tolerant Control for a Quadrotor UAV Attitude Stabilization 35

Adaptive Dynamic Programming-based Fault Tolerant Control fo...

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35th Chinese Control and Decision Conference (CCDC)

作者： Yang, Shaoyu Li, Bo Liu, Hui Zhang, Haichao Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 201306 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9798350334722

This paper addresses an attitude stabilization control problem for a quadrotor UAV under actuator faults via adaptive dynamic programming. First, a Hamilton-Jacobi-Bellman equation is solved by utilizing an adaptive critical neural network (NN) structure in the absence of actuator faults, and an approximate optimal control policy is derived. Meanwhile, a novel NN weight update law is proposed based on the Bellman error upper bound function, which not only has simple structure but also relieve the persistent excitation condition. Then, an adaptive law-based feedforward compensator is proposed to restrain actuator faults. Moreover, the ultimately uniformly bounded stability of the closed-loop system is proved by the development of Lyapunov methodology. Finally, the numerical simulation results verify the feasibility and effectiveness of the proposed control scheme.

关键词： adaptive dynamic programming Attitude Stabilization Fault Tolerant Control Quadrotor UAV

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adaptive dynamic programming-Based Sliding Mode Optimal Position-Force Control for Reconfigurable Manipulators with Uncertain Disturbance 32

Adaptive Dynamic Programming-Based Sliding Mode Optimal Posi...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhu, Xinye Ma, Bing Dong, Bo Liu, Keping Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

ISBN: (纸本)9781728158556

In this paper, a sliding mode optimal position-force control method is proposed for constrained reconfigurable manipulators with disturbance based on adaptive dynamic programming (ADP) method and policy iteration (PI) algorithm. The sliding mode control is developed to compensate the model uncertainties, and the improved performance index function includes the sliding mode function and disturbance function. Then, the position-force control problem of the reconfigurable manipulator system under disturbance is transformed into an optimal control issue. The solution of Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by using ADP and PI methods, and then the approximated sliding mode optimal control policy can be derived by constructing the critic neural network (NN). The closed-loop robotic system is proved to be asymptotic stability by using Lyapunov theory. Finally, simulations are provided to demonstrate the effectiveness of the proposed method.

关键词： Reconfigurable manipulators adaptive dynamic programming Sliding mode optimal position-force control Policy iteration algorithm Critic neural network

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adaptive dynamic programming for Cooperative Control with Incomplete Information

Adaptive Dynamic Programming for Cooperative Control with In...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Koepf, Florian Ebbert, Sebastian Flad, Michael Hohmann, Soeren Karlsruhe Inst Technol Inst Control Syst IRS Karlsruhe Germany

ISBN: (纸本)9781538666500

There is a trend towards interconnected and complex dynamical systems that are controlled by more than one controller. Due to the coupling of the controllers by means of the system, these interacting controllers need to consider not only the system dynamics but also the influence of each other. However, in realistic scenarios, they usually do not exchange all the information concerning their parameters and control laws and an exact model of the system dynamics is often hard to obtain. This is why we consider the challenging setting where the controllers have no access neither to the parameters of each other nor to the system dynamics. The controller design is quite difficult in this scenario, as the final system configuration is not known during the design process. In this complex scenario, we propose algorithms where each controller uses adaptive dynamic programming to adapt its control law. Here, each controller strives for reaching its individual control objectives, a setting which can be formulated as a coupled optimization problem, respectively a dynamic game. As an example, we consider a vehicle model with two lateral controllers. With our proposed algorithms, the controllers converge successfully to a solution of the coupled optimization problem without knowing the parameters of each other and the system dynamics.

关键词： Cooperative Control adaptive dynamic programming adaptive Optimal Control Game Theory Reinforcement Learning

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adaptive dynamic programming for Human Postural Balance Control 24th

Adaptive Dynamic Programming for Human Postural Balance Cont...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Mauro, Eric Bian, Tao Jiang, Zhong-Ping NYU Dept Elect & Comp Engn Tandon Sch Engn 5 Metrotech Ctr Brooklyn NY 11201 USA

ISBN: (纸本)9783319700939;9783319700922

This paper provides a basis for studying human postural balance control about upright stance using adaptive dynamic programming (ADP) theory. Previous models of human sensorimotor control rely on a priori knowledge of system dynamics. Here, we provide an alternative framework based on the ADP theory. The main advantage of this new framework is that the system model is no longer required, and an adaptive optimal controller is obtained directly from input and state data. We apply this theory to simulate human balance behavior, and the obtained results are consistent with the experiment data presented in the past literature.

关键词： Optimal control Motor learning adaptive dynamic programming

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adaptive dynamic programming for Optimal Control of van der Pol Oscillator 30

Adaptive Dynamic Programming for Optimal Control of van der ...

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30th Chinese Control and Decision Conference (CCDC)

作者： Ji, Zheng Lou, Xuyang Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781538612446

adaptive dynamic programming has been considered as one of the effective methods to solve the optimal control of nonlinear systems and it has lots of applications in academic research and engineering. Firstly, we formulate the optimal control of van der Pol oscillator with the least energy consumption. Then, using an adaptive dynamic programming method to dynamically solve the approximate solution of this optimal control problem. A simplified neural network model is used to approximate the cost function. In addition, an admissible initial control policy for van der Pol oscillator is designed and Lyapunov stability criterion is established. On the basis of predecessors, we present a moving-data window recursive least-square method, which can overcome the computational difficulties and the problem that the parameter of oscillator maybe unknown and various slowly. Finally, the implementation steps of the method arc proposed, and the feasibility is illustrated.

关键词： adaptive dynamic programming van der Pol oscillator neural network moving-data window recursive least-square method

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adaptive dynamic programming-Based Attitude Optimal Tracking Control for a Quadrotor with Unmeasured Velocities and Model Uncertainties

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Guidance, Navigation and Control 2024年第2期4卷 25-46页

作者： Junrui Guo Xiaoyang Gao Tieshan Li School of Automation Engineering University of Electronic Science and Technology of China Chengdu Sichuan 611731P.R.China

This paper proposes an optimal output feedback tracking control scheme of the quadrotor unmanned aerial vehicle(UAV)attitude system with unmeasured angular velocities and model ***,neural network(NN)is used to approximate the model ***,an NN velocity observer is established to estimate the unmeasured angular ***,a quadrotor output feedback attitude optimal tracking controller is designed,which consists of an adaptive controller designed by backstepping method and an optimal compensation term designed by adaptive dynamic *** signals in the closed-loop system are proved to be ***,numerical simulation example shows that the quadrotor attitude tracking scheme is effective and feasible.

关键词： Quadrotor optimal tracking control neural network velocity observer model uncertainties adaptive dynamic programming

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