Event-Triggered Single-Network ADP for Zero-Sum Game of Unknown Nonlinear Systems with Constrained Input

In this paper, an event-triggered adaptive dynamic programming (ADP) method is proposed to deal with the H∞ problem unknown and constrained input. Firstly, H∞-constrained regarded as two-player zero-sum game nonquadratic value function. Secondly, we develop Hamilton–Jacobi–Isaacs(HJI) equation, ADP solve HJI which equivalent solving Nash saddle point of game. An event-based single-critic neural network (NN) applied obtain optimal function, reduces communication resource computational cost algorithm implementation. For control, a triggering condition level disturbance attenuation developed limit number sampling states, avoids Zeno behavior by proving existence events minimum interval. It proved theoretically that closed-loop system asymptotically stable, critic NN weight error uniformly ultimately boundedness (UUB). The learning performance verified two examples.