Title :
Robust Adaptive Dynamic Programming With an Application to Power Systems
Author :
Yu Jiang ; Zhong-Ping Jiang
Author_Institution :
Dept. of Electr. & Comput. Eng., Polytech. Inst. of New York Univ., Brooklyn, OH, USA
Abstract :
This brief presents a novel framework of robust adaptive dynamic programming (robust-ADP) aimed at computing globally stabilizing and suboptimal control policies in the presence of dynamic uncertainties. A key strategy is to integrate ADP theory with techniques in modern nonlinear control with a unique objective of filling up a gap in the past literature of ADP without taking into account dynamic uncertainties. Neither the system dynamics nor the system order are required to be precisely known. As an illustrative example, the computational algorithm is applied to the controller design of a two-machine power system.
Keywords :
control system synthesis; dynamic programming; nonlinear control systems; power system control; suboptimal control; uncertain systems; controller design; dynamic uncertainty; nonlinear control; robust adaptive dynamic programming; suboptimal control; system dynamics; two-machine power system; Adaptive systems; Generators; Optimal control; Power system dynamics; Power system stability; Robustness; Uncertainty; Nonlinear uncertain systems; optimal control; reinforcement learning;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2013.2249668