期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 19, 期 7, 页码 1243-1252出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2008.2000204
关键词
actuator saturation; H(infinity) control; policy iterations; zero-sum games
类别
资金
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [0801330] Funding Source: National Science Foundation
In this paper, neural networks are used along with two-player policy iterations to solve for the feedback strategies of a continuous-time zero-sum game that appears in L(2)-gain optimal control, suboptimal H(infinity) control, of nonlinear systems affine in input with the control policy having saturation constraints. The result is a closed-form representation, on a prescribed compact set chosen a priori, of the feedback strategies and the value function that solves the associated Hamilton-Jacobi-Isaacs (HJI) equation. The closed-loop stability, L(2)-gain disturbance attenuation of the neural network saturated control feedback strategy, and uniform convergence results are proven. Finally, this approach is applied to the rotational/translational actuator (RTAC) nonlinear benchmark problem under actuator saturation, offering guaranteed stability and disturbance attenuation.
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