Data-Driven Adaptive Dynamic Programming for Optimal Control of Continuous-Time Multicontroller Systems With Unknown Dynamics

Abstract

This paper investigates the optimal control of continuous-time multi-controller systems with completely unknown dynamics using data-driven adaptive dynamic programming (DD-ADP). In this investigation, all controllers take actions together as a team, and they have precisely the same cost function, which is actually a fully cooperative game. According to optimal control theory, the HJB equation corresponding to the fully cooperative game is derived. To obtain the solution to HJB equation, a model-based policy iteration (PI) algorithm is first presented. On the basis of the PI algorithm, a DD-ADP algorithm without requiring the system dynamics is developed, and the neural networks (NNs) implementation scheme of the developed DD-ADP algorithm is given. Stability and convergence analysis are derived by Lyapunov theory. Finally, numerical simulation examples on linear and nonlinear multi-controller systems demonstrate the effectiveness of the designed scheme.

Keywords

• Adaptive dynamic programming
• fully cooperative games
• neural networks
• multi-controller systems
• unknown dynamics