Abstract
This article proposes a decentralized, online optimal feedback control strategy to optimally stabilize active loads in islanded DC microgrids (DCMGs). Each active load is modeled as a control affine dynamical system with an interconnected coupling term in the energy and admittance domain. Then the decentralized, constrained input of each active load is obtained online in the feedback form to minimize the infinite horizon quadratic state cost and non quadratic control effort while supplying the active load demand. The approximate dynamic programming (ADP) approach is employed to solve the feedback optimal control problem online by successive approximation of the value function via two linear in the parameter (LIP) neural networks (NNs). A concurrent reinforcement learning (RL) based method is introduced to update the unknown weights in the NN. The convergence of the unknown weights to a neighborhood of the optimal weights and the uniformly ultimately bounded stability of the system are analyzed by a Lyapunov direct method. Series of simulation results are presented to demonstrate the effectiveness and the applicability of the proposed concept.
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