Approximate Dynamic Programming for Event-Driven H∞ Constrained Control
研究了同时存在对称和非对称约束的事件驱动H∞控制问题，通过近似动态规划将问题转化为零和博弈，并用单个评论神经网络求解，降低了计算量。
We study the dynamic event-driven H∞ constrained control problem through approximate dynamic programming (ADP). Differing from the existing literature considering systems with either symmetric constraints or asymmetric constraints, we consider the two different constraints simultaneously. Initially, by constructing a generalized nonquadratic value function, we transform the H∞ constrained control problem into an unconstrained two-player zero-sum game. Then, we present an event-driven Hamilton–Jacobi–Isaacs equation (ED-HJIE) corresponding to the zero-sum game for lowering down the computational load. To solve the ED-HJIE, we propose a dynamic triggering mechanism together with a sole critic neural network (CNN) being built under the ADP framework. The CNN’s weights are tuned via the gradient descent approach. After that, we prove uniform ultimate boundedness of the closed-loop system and the CNN’s weight estimation error via Lyapunov’s method. Finally, we separately use an F16 aircraft plant and an inverted pendulum system to validate the present theoretical claims.