We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Event-Triggered Single-Network ADP for Zero-Sum Game of Unknown Nonlinear Systems with Constrained Input.
- Authors
Peng, Binbin; Cui, Xiaohong; Cui, Yang; Chen, Wenjie
- Abstract
In this paper, an event-triggered adaptive dynamic programming (ADP) method is proposed to deal with the H ∞ problem with unknown dynamic and constrained input. Firstly, the H ∞ -constrained problem is regarded as the two-player zero-sum game with the nonquadratic value function. Secondly, we develop the event-triggered Hamilton–Jacobi–Isaacs(HJI) equation, and an event-triggered ADP method is proposed to solve the HJI equation, which is equivalent to solving the Nash saddle point of the zero-sum game. An event-based single-critic neural network (NN) is applied to obtain the optimal value function, which reduces the communication resource and computational cost of algorithm implementation. For the event-triggered control, a triggering condition with the level of disturbance attenuation is developed to limit the number of sampling states, and the condition avoids Zeno behavior by proving the existence of events with minimum triggering interval. It is proved theoretically that the closed-loop system is asymptotically stable, and the critic NN weight error is uniformly ultimately boundedness (UUB). The learning performance of the proposed algorithm is verified by two examples.
- Subjects
ZERO sum games; NONLINEAR systems; DYNAMIC programming; CLOSED loop systems; ADAPTIVE control systems; REINFORCEMENT learning; SADDLEPOINT approximations
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 4, p2140
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13042140