Multiple Model Adaptive Tracking Control Based on Adaptive Dynamic Programming

Abstract

Adaptive dynamic programming (ADP) has been tested as an effective method for optimal control of nonlinear system. However, as the structure of ADP requires control input to satisfy the initial admissible control condition, the control performance may be deteriorated due to abrupt parameter change or system failure. In this paper, we introduce the multiple models idea into ADP, multiple subcontrollers run in parallel to supply multiple initial conditions for different environments, and a switching index is set up to decide the appropriate initial conditions for current system. By taking this strategy, the proposed multiple model ADP achieves optimal control for system with jumping parameters. The convergence of multiple model adaptive control based on ADP is proved and the simulation shows that the proposed method can improve the transient response of system effectively.