Title: Model-Free Adaptive Control for Unknown Nonlinear Zero-Sum Differential Game
Authors: Zhong, XN; He, HB; Wang, D; Ni, Z
Author Full Names: Zhong, Xiangnan; He, Haibo; Wang, Ding; Ni, Zhen
Source: IEEE TRANSACTIONS ON CYBERNETICS, 48 (5):1633-1646; 10.1109/TCYB.2017.2712617 MAY 2018
Language: English
Abstract: In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online learning algorithm is proposed based on the GDHP method to solve the Hamilton-Jacobi-Isaacs equation associated with H-infinity optimal regulation control problem. By setting backward one step of the definition of performance index, the requirement of system dynamics, or an identifier is relaxed in the proposed method. Then, three neural networks are established to approximate the optimal saddle point feedback control law, the disturbance law, and the performance index, respectively. The explicit updating rules for these three neural networks are provided based on the data generated during the online learning along the system trajectories. The stability analysis in terms of the neural network approximation errors is discussed based on the Lyapunov approach. Finally, two simulation examples are provided to show the effectiveness of the proposed method.
ISSN: 2168-2267
eISSN: 2168-2275
IDS Number: GB7II
Unique ID: WOS:000429247700023
PubMed ID: 28727566