For a Class of Discrete-Time Nonlinear System Based on Iterative HDP

2013 ◽  
Vol 325-326 ◽  
pp. 1493-1497
Author(s):  
Chun Ning Song ◽  
Zhou Hu ◽  
Xiao Feng Lin
Keyword(s):  
Nonlinear System ◽  
Discrete Time ◽  
Tracking Control ◽  
Optimal Time ◽  
Time Varying ◽  
Optimal Tracking ◽  
The Neural Network ◽  
Optimal Regulator ◽  
And Performance ◽  
The Given

In this paper, the iterative HDP-based optimal tracking algorithm for discrete-time nonlinear systems is studied. The optimal tracking control problem of original nonlinear system is transformed to the optimal regulator problem by transforming the system and performance index in this algorithm, then using the HDP iteration to solve the optimal regulation problem. Finally, the neural network implementation for the algorithm is detailedly elaborated, and the given simulation results show the effectiveness of the optimal time-varying tracking based on iterative HDP algorithm.

scholarly journals MTN Optimal Tracking Control of SISO Nonlinear Time-Varying Discrete-Time Systems without Mechanism Models

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Jiao-Jun Zhang ◽  
Hong-Sen Yan
Keyword(s):  
Optimal Control ◽  
Online Learning ◽  
Discrete Time ◽  
Output Signal ◽  
Tracking Control ◽  
Control Law ◽  
Time Varying ◽  
Discrete Time Systems ◽  
Time Systems ◽  
The Given

Nonlinear time-varying systems without mechanism models are common in application. They cannot be controlled directly by the traditional control methods based on precise mathematical models. Intelligent control is unsuitable for real-time control due to its computation complexity. For that sake, a multidimensional Taylor network (MTN) based output tracking control scheme, which consists of two MTNs, one as an identifier and the other as a controller, is proposed for SISO nonlinear time-varying discrete-time systems with no mechanism models. A MTN identifier is constructed to build the offline model of the system, and a set of initial parameters for online learning of the identifier is obtained. Then, an ideal output signal is selected relative to the given reference signal. Based on the system identification model, Pontryagin minimum principle is introduced to obtain the numerical solution of the optimal control law for the system relative to the given ideal output signal, with the corresponding optimal output taken as the desired output signal. A MTN controller is generated automatically to fit the numerical solution of the optimal control law using the conjugate gradient (CG) method, and a set of initial parameters for online learning of the controller is obtained. An adaptive back propagation (BP) algorithm is developed to adjust the parameters of the identifier and controller in real time, and the convergence for the proposed learning algorithm is verified. Simulation results show that the proposed scheme is valid.

Reinforcement -learning for optimal tracking control of linear discrete-time systems with unknown dynamics

Automatica ◽  
2014 ◽  
Vol 50 (4) ◽  
pp. 1167-1175 ◽  
Author(s):  
Bahare Kiumarsi ◽  
Frank L. Lewis ◽  
Hamidreza Modares ◽  
Ali Karimpour ◽  
Mohammad-Bagher Naghibi-Sistani
Keyword(s):  
Reinforcement Learning ◽  
Discrete Time ◽  
Tracking Control ◽  
Optimal Tracking ◽  
Optimal Tracking Control ◽  
Discrete Time Systems ◽  
Time Systems

scholarly journals Decentralized Reinforcement Learning Robust Optimal Tracking Control for Time Varying Constrained Reconfigurable Modular Robot Based on ACI andQ-Function

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Bo Dong ◽  
Yuanchun Li
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Control Theory ◽  
Continuous Time ◽  
Tracking Control ◽  
Control Policy ◽  
Time Varying ◽  
Optimal Tracking ◽  
Tracking Controller ◽  
Global Uncertainty

A novel decentralized reinforcement learning robust optimal tracking control theory for time varying constrained reconfigurable modular robots based on action-critic-identifier (ACI) and state-action value function (Q-function) has been presented to solve the problem of the continuous time nonlinear optimal control policy for strongly coupled uncertainty robotic system. The dynamics of time varying constrained reconfigurable modular robot is described as a synthesis of interconnected subsystem, and continuous time state equation andQ-function have been designed in this paper. Combining with ACI and RBF network, the global uncertainty of the subsystem and the HJB (Hamilton-Jacobi-Bellman) equation have been estimated, where critic-NN and action-NN are used to approximate the optimalQ-function and the optimal control policy, and the identifier is adopted to identify the global uncertainty as well as RBF-NN which is used to update the weights of ACI-NN. On this basis, a novel decentralized robust optimal tracking controller of the subsystem is proposed, so that the subsystem can track the desired trajectory and the tracking error can converge to zero in a finite time. The stability of ACI and the robust optimal tracking controller are confirmed by Lyapunov theory. Finally, comparative simulation examples are presented to illustrate the effectiveness of the proposed ACI and decentralized control theory.

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