Robust Adaptive Dynamic Programming: A New Tool for Optimal Nonlinear Control Design
Speaker：Prof. Zhong-Ping Jiang, New York University
Chair：Prof. Hong QIAO
Time：April,21th, 2016（Thursday） 10:30 – 11:30am
Venue：1st Meeting Room, 3rd Floor, Intelligence Building
Abstract
Bellman's Dynamic Programming is a powerful theory for addressing multi-stage decision making problems, and has been used to solve the optimal control problem. However, it suffers from the ‘curse of dimensionality' and the ‘curse of modeling’. In this talk, a new framework of robust adaptive dynamic programming is proposed to relax these two restrictions and used to design a new class of adaptive optimal nonlinear controllers. We will show that robust adaptive dynamic programming (for short, RADP) is also a significant extension of the existing work in approximate/adaptive dynamic programming (ADP) in that the order of the dynamic processes in question is not known. The mismatch between the real plant and the simplified model is called dynamic uncertainty.
If time permits, we will briefly discuss our recent research in global ADP for nonlinear systems, which differs fundamentally from the present literature of ADP for nonlinear systems which yield only semi-global or local stability for the closed-loop control systems. Robustness to both parametric and dynamic uncertainties is a key feature of RADP as a new tool for optimal nonlinear control design. Applications to power systems and biological motor control are presented to illustrate the effectiveness of RADP.