Constrained event-driven policy iteration design for nonlinear discrete time systems

Abstract

This paper studies an adaptive critic design (ACD) solution based on the event-driven mechanism for the constrained state and input system with external disturbance. At first, the barrier function is presented to construct the transformation of the constrained state system, and then, a suitable value function with non-quadratic utility function is designed according to the auxiliary system to acquire the control sequence in the infinity domain. After that, the comprehensive problem is transited into solving the Hamilton–Jacobi-Isaacs (HJI) equation. Then, the optimal control policy pair is obtained by policy iteration (PI) via double event-driven scheme. The three critic, action and disturbance neural networks (NNs) are built up to manipulate the data online concurrently to approximate the control pair sequence, and the ultimately uniformly bounded (UUB) proof of the estimation error generated by NNs is given. The last but not least, two sets of contrast simulations are analyzed to demonstrate the effectiveness of the proposed algorithm, which makes the model reduce the data transmission density and decrease the update times of control policies under the premise of system stability and ideal performance.