Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning

作     者：Modares, Hamidreza Lewis, Frank L. 

作者机构：Univ Texas Arlington Res Inst Ft Worth TX 76118 USA 

出 版 物：《AUTOMATICA》 (自动学)

年 卷 期：2014年第50卷第7期

页      面：1780-1792页

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

基　　金：NSF [ECCS-1128050] ONR [N00014-13-1-0562] AFOSREOARD [13-3055] ARO [W911NF-11-D-0001] China NNSF China Education Ministry Project 111 [B08015] 

主　　题：Optimal tracking control Integral reinforcement learning Input constrainers Neural networks 

摘      要：In this paper, a new formulation for the optimal tracking control problem (OTCP) of continuous-time nonlinear systems is presented. This formulation extends the integral reinforcement learning (IRL) technique, a method for solving optimal regulation problems, to learn the solution to the OTCP. Unlike existing solutions to the OTCP, the proposed method does not need to have or to identify knowledge of the system drift dynamics, and it also takes into account the input constraints a priori. An augmented system composed of the error system dynamics and the command generator dynamics is used to introduce a new nonquadratic discounted performance function for the OTCP. This encodes the input constrains into the optimization problem. A tracking Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic performance function is derived which gives the optimal control solution. An online IRL algorithm is presented to learn the solution to the tracking HJB equation without knowing the system drift dynamics. Convergence to a near-optimal control solution and stability of the whole system are shown under a persistence of excitation condition. Simulation examples are provided to show the effectiveness of the proposed method. (C) 2014 Elsevier Ltd. All rights reserved.