Neural adaptive chaotic control with constrained input using state and output feedback

Gao, Shigen; Dong, Hairong; Sun, Xubin; Ning, Bin

doi:10.1088/1674-1056/24/1/010501

Cited by 8 publications

(3 citation statements)

References 38 publications

(42 reference statements)

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“…For a nonlinear system with input constraints [3,4], an event-triggered-based non-zero-sum (NZS) game approximate optimal control method based on integral reinforcement learning (IRL) is proposed in this paper to solve the trajectory tracking control problem. For the nonlinear system with input constraints, Gao et al [5] developed a class of adaptive control methods with input constraints and constructed an auxiliary system to maintain the stability. Meng et al [6] suggested a cooperative control method for a multi-agent system, which transformed the constrained tracking control issues into an unconstrained output feedback control problem.…”

Section: Introductionmentioning

confidence: 99%

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Dong,

Ding,

et al. 2024

Meas. Sci. Technol.

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Under the event-triggered mechanism, a non-zero-sum game optimal tracking control method for modular robot manipulators (MRMs) system with input constraints is proposed by using the adaptive dynamic programming (ADP) method based on integral reinforcement learning (IRL). First, the dynamic model of MRMs system, based on joint torque feedback (JTF) technology, consists of n joint subsystem which is related to interconnected dynamic coupling (IDC). Second, we design the robust compensation controller to handle the model known term and optimal compensation controller to deal with the uncertainty term caused by the IDC and friction, respectively. In addition, a nonlinear disturbance observer is established to dispose the negative effect caused by uncertain sensor output disturbance. Third, based on differential game theory, we transform the optimal tracking control problem of MRMs system into an n-player NZS game problem. Then, the IRL-based ADP method is adopted that relax the need for system partial unknown dynamic information and only critic neural network (NN) is used to solve the coupled Hamilton-Jacobi (HJ) equation, so as to obtain the optimal control policy. Then by Lyapunov theory, the tracking error of MRMs system is demonstrated to be uniformly ultimately bounded (UUB). Finally, the effectiveness and superiority of the proposed algorithm are verified through experiments.

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Section: Introductionmentioning

confidence: 99%

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Dong,

Ding,

et al. 2024

Meas. Sci. Technol.

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“…On the other hand, owing to the development of computer technology, many algorithms of neural networks arise at the historic moment and have attracted broad attention. [27][28][29][30][31][32] Among them, RBF neural networks have shown great computational superiority in solving various partial differential equations, [33][34][35][36] especially the FPK equation. [37,38] Wang et al proposed the use of the RBF neural networks to calculate the transient solutions of FPK equations [38] and first-passage problems [37] in random vibrations, setting up a great wave of solving nonlinear random vibration problems.…”

Section: Introductionmentioning

confidence: 99%

Semi-analytical stationary response prediction for multi-dimensional quasi-Hamiltonian systems

Chen

Yuan

et al. 2022

Chinese Phys. B

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In science and engineering, the majority of non-linear stochastic systems can be represented as the quasi-Hamiltonian systems. Moreover, the corresponding Hamiltonian system offers two concepts of integrability and resonance that can fully describe the global relationship among the degrees-of-freedom (DOFs) of the system. This paper proposes an effective and promising approximate semi-analytical method for the stationary response of multi-dimensional quasi-Hamiltonian systems. To be specific, the trial solution of the reduced Fokker-Plank-Kolmogorov (FPK) equation constructs with radial basis function (RBF) neural networks. Then taking into account the residual generated by substituting the trial solution into the reduced FPK equation, a loss function is constructed by combining random sampling technique, and the unknown weight coefficients are optimized by minimizing the loss function through the Lagrange multiplier method. Moreover, an efficient sampling strategy is employed to promote the implementation of algorithms. Finally, two numerical examples are studied in detail, and all the semi-analytical solutions are in contrast with Monte Carlo simulations (MCS) results. The results indicate that the complex non-linear dynamic features of the system response can be captured through the proposed scheme accurately.

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“…[1,2] Lots of control methods have been developed, such as the impulsive control method, [3][4][5] the adaptive dynamic programming method, [6,7] and neural adaptive control method. [8] In addition, the optimal tracking control problem is often encountered in the industrial process, and so it has recently been the research focus of many researchers. [9][10][11] In recent years, the adaptive dynamic programming (ADP) method has attracted a great deal of attention in the control field, which is one of the most useful intelligent control methods for solving nonlinear Hamilton-Jacobi-Bellman (HJB) equations.…”

Section: Introductionmentioning

confidence: 99%

Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

Wei¹,

Song²,

Sun³

et al. 2015

Chinese Phys. B

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This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.

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Neural adaptive chaotic control with constrained input using state and output feedback

Abstract: Neural adaptive chaotic control with constrained input using state and output feedback * Gao Shi-Gen(高士根) a) , Dong Hai-Rong(董海荣) a) † , Sun Xu-Bin(孙绪彬) b) , and Ning Bin(宁滨) a) a)

Cited by 8 publications

References 38 publications

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Semi-analytical stationary response prediction for multi-dimensional quasi-Hamiltonian systems

Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

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Neural adaptive chaotic control with constrained input using state and output feedback

Abstract: Neural adaptive chaotic control with constrained input using state and output feedback * Gao Shi-Gen(高士根) a) , Dong Hai-Rong(董海荣) a) † , Sun Xu-Bin(孙绪彬) b) , and Ning Bin(宁 滨) a) a)

Cited by 8 publications

References 38 publications

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Semi-analytical stationary response prediction for multi-dimensional quasi-Hamiltonian systems

Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

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Abstract: Neural adaptive chaotic control with constrained input using state and output feedback * Gao Shi-Gen(高士根) a) , Dong Hai-Rong(董海荣) a) † , Sun Xu-Bin(孙绪彬) b) , and Ning Bin(宁滨) a) a)