Distributed Optimal Coordination Control for Continuous-Time Nonlinear Multi-Agent Systems With Input Constraints
Yunhong Deng, Jun Xiao, Qinglai Wei · 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) · 2020
This paper is concerned with an optimal coordination control problem for nonlinear multi-agent systems (MASs) with constraints of the control inputs. The idea of daptive dynamic programming (ADP) algorithm is to use the policy iteration to solve the coupled Hamilton-Jacobi equations. First, a suitable non-quadratic functional is introduced into the cost function to transform the question into an optimization problem. Second, a distributed control law is designed for each agent, which aims that the cost function of the MASs converge to Nash equilibrium. Next, the analysis of the convergence is indicated that the iterative cost functions of nonlinear multi-agent systems is convergent. Neural network (NNs) are used to approximate the cost functions for the calculation of the control laws. Finally, simulation results show the effectiveness of the coordination control algorithm.