Data‐based distributed consensus optimal control for nonlinear multi‐agent systems under switching topology

Abstract

AbstractThis article investigates the issue of data‐based distributed consensus optimal control for a class of affine nonlinear multi‐agent systems (MASs) under switching topology with external disturbances. With the help of the game theory, the distributed adaptive optimal consensus control issue can be formulated into a zero‐sum (ZM) game problem. In control design, a data‐based integral reinforcement learning (IRL) algorithm is used to solve the coupled Hamilton–Jacobi–Isaac (HJI) equation with unknown drift dynamics. Meanwhile, to relax the persistent excitation (PE) condition in the traditional optimal control design, the experience replay (ER) technique is introduced. Combining IRL algorithm and single critic neural network (NN), a distributed adaptive optimal consensus control approach is designed. The stability of the closed‐loop system is proved by combining the Lyapunov stability theory and the average dwell time method. Finally, a simulation example is given to illustrate the effectiveness of the developed optimal consensus control approach.