Online Optimal DLQR-DFIG Control System Design via Recursive Least-Square Approach and State Heuristic Dynamic Programming for Approximate Solution of the HJB Equation

Our aim in this paper is to present a novel method for online optimal control system design via state heuristic dynamic programming (HDP) to approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equation by means of the recursive least-square (RLS) approach. Because the randomness nature associated to primary energy sources, the control of eolic and solar energy systems demands methods and technics that are suitable with the high degree of the environment uncertainties. The reinforcement learning (RL) and approximate dynamic programming (ADP) approaches furnish the key ideas and the mathematical formulations to develop optimal control system methods and strategies for alternative energy systems. We are proposing a online design method to establish control strategies for the the main unit of a eolic system that is the doubly fed induction generator (DFIG). The performance of proposed method is evaluated via computational experiments for discrete time HDP algorithms that map eigenstructure assignments in the stable Z-plane.

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