Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming

Wang, Ding; Liu, Derong; Wei, Qinglai; Zhao, Dan; Jin, Ning

doi:10.1016/j.automatica.2012.05.049

Cited by 355 publications

(123 citation statements)

References 24 publications

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“…It had been proven in [15] that ( ) i k J x is monotone nondecreasing and the algorithm will converge to the optimal while 0 ( ) J x is initiated as zero.…”

Section: Adp Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

A Stochastic Optimal Control Method of Home Energy Based on Adaptive Dynamic Programming

Liu¹,

Zhang²,

Chen³

2016

Proceedings of the 2016 7th International Conference on Mechatronics, Control and Materials (ICMCM 2016)

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Abstract. Home energy optimization (HEO) is an infinite horizon optimization problem, only adaptive dynamic programming (ADP) can solve infinite horizon optimization problem. But due to the existence of time-varying parameters, ADP can not converge to the optimal and can not be applied directly to the optimal control of home energy. According to the periodic characteristics of the time-varying parameters, a stochastic optimal control method is proposed in the paper based on ADP. The influence of time-varying parameters in the iteration of ADP is eliminated by the proposed method. Simulation results show the performance of the proposed method.

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“…It had been proven in [15] that ( ) i k J x is monotone nondecreasing and the algorithm will converge to the optimal while 0 ( ) J x is initiated as zero.…”

Section: Adp Algorithmmentioning

confidence: 99%

“…The convergence and optimality of ADP without time-varying parameter are proven [10][14] [15]. But ADP can not be applied directly to HEO as there are time-varying parameters within HEO.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Optimal Control Method of Home Energy Based on Adaptive Dynamic Programming

Liu¹,

Zhang²,

Chen³

2016

Proceedings of the 2016 7th International Conference on Mechatronics, Control and Materials (ICMCM 2016)

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show abstract

“…In [64], three neural networks were constructed for an iterative ADP, such that optimal feedback control of a discrete-time affine nonlinear system could be realized. In [65], a globalized dual heuristic programming was presented to address the optimal control of discrete-time systems. In each iteration, three neural networks were used to learn the cost function and the unknown nonlinear systems.…”

Section: Nn Based Adaptive Dynamicmentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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“…[29] Keith Dupree used the implicit learning capabilities to learn the dynamics asymptotically and studied the optimal control of uncertain nonlinear Euler-Lagrange systems. [30] Wu et al presented a min-max optimal control of linear systems with uncertainty and terminal state constraints, which is solved through solving a sequence of semi-definite programming problems. [31] However, the approach model is a complex nonlinear system and the terminal states of the platform are especially uncertain.…”

Section: Control Strategy Of Tsrmentioning

confidence: 99%

An Effective Approach Control Scheme for the Tethered Space Robot System

Meng

Huang

2014

International Journal of Advanced Robotic Systems

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The tethered space robot system (TSR), which is composed of a platform, a gripper and a space tether, has great potential in future space missions. Given the relative motion among the platform, tether, gripper and the target, an integrated approach model is derived. Then, a novel coordinated approach control scheme is presented, in which the tether tension, thrusters and the reaction wheel are all utilized. It contains the open-loop trajectory optimization, the feedback trajectory control and attitude control. The numerical simulation results show that the rendezvous between TSR and the target can be realized by the proposed coordinated control scheme, and the propellant consumption is efficiently reduced. Moreover, the control scheme performs well in the presence of the initial state's perturbations, actuator characteristics and sensor errors.

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Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming

Cited by 355 publications

References 24 publications

A Stochastic Optimal Control Method of Home Energy Based on Adaptive Dynamic Programming

A Stochastic Optimal Control Method of Home Energy Based on Adaptive Dynamic Programming

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

An Effective Approach Control Scheme for the Tethered Space Robot System

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