Continuous-Time Distributed Policy Iteration for Multicontroller Nonlinear Systems

In this article, a novel distributed policy iteration algorithm is established for infinite horizon optimal control problems of continuous-time nonlinear systems. In each iteration of the developed distributed policy iteration algorithm, only one controller's control law is updated and the other controllers' control laws remain unchanged. The main contribution of the present algorithm is to improve the iterative control law one by one, instead of updating all the control laws in each iteration of the traditional policy iteration algorithms, which effectively releases the computational burden in each iteration. The properties of distributed policy iteration algorithm for continuous-time nonlinear systems are analyzed. The admissibility of the present methods has also been analyzed. Monotonicity, convergence, and optimality have been discussed, which show that the iterative value function is nonincreasingly convergent to the solution of the Hamilton-Jacobi-Bellman equation. Finally, numerical simulations are conducted to illustrate the effectiveness of the proposed method.

Automated summary

A novel distributed policy iteration algorithm is proposed for infinite horizon optimal control problems of continuous-time nonlinear systems. By improving the control law iteratively one by one, the computational burden in each iteration is effectively reduced. The properties of the distributed policy iteration algorithm, including monotonicity, convergence, and optimality, have been analyzed in detail.