PROJECT TITLE :

Global Adaptive Dynamic Programming for Continuous-Time Nonlinear Systems

ABSTRACT:

This paper presents a novel technique of world adaptive dynamic programming (ADP) for the adaptive optimal control of nonlinear polynomial systems. The strategy consists of relaxing the problem of solving the Hamilton-Jacobi-Bellman (HJB) equation to an optimization drawback, which is solved via a replacement policy iteration methodology. The proposed method distinguishes from previously known nonlinear ADP strategies in that the neural network approximation is avoided, giving rise to vital computational improvement. Rather than semiglobally or domestically stabilizing, the resultant management policy is globally stabilizing for a general category of nonlinear polynomial systems. Furthermore, within the absence of the a priori data of the system dynamics, an on-line learning methodology is devised to implement the proposed policy iteration technique by generalizing the current ADP theory. Finally, 3 numerical examples are provided to validate the effectiveness of the proposed technique.