Data-Based Nonaffine Optimal Tracking Control Using Iterative DHP Approach ⁎This work was supported in part by Beijing Natural Science Foundation under Grant JQ19013, and in part by the National Natural Science Foundation of China under Grant 61773373 and Grant 61533017. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Ha, Mingming; Wang, Ding; Liu, Derong
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 4246
• Page End: 4251
• Publication Date: 2020
• Subjects: Adaptive dynamic programming -- data-based optimal tracking control -- iterative dual heuristic dynamic programming -- neural network; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2473
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, a data-based optimal tracking control approach is developed by involving the iterative dual heuristic dynamic programming algorithm for nonaffine systems. In order to gain the steady control corresponding to the desired trajectory, a novel strategy is established with regard to the unknown system function. Then, according to the iterative adaptive dynamic programming algorithm, the updating formula of the costate function and the new optimal control policy for unknown nonaffine systems are provided to solve the optimal tracking control problem. Moreover, three neural networks are used to facilitate the implementation of the proposed algorithm. In order to improve the accuracy of the steady control corresponding to the desired trajectory, we employ a model network to directly approximate the unknown system function instead of the error dynamics. Finally, the effectiveness of the proposed method is demonstrated through a simulation example.