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Adaptive Neural Network Output Feedback Optimal Saturation Control for Single-Phase Photovoltaic Grid-Connected Power Systems
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DOI：10.1002/acs.70107.png)
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Abstract
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The adaptive neural network (NN) output feedback optimal saturation control scheme is investigated for a single-phase photovoltaic (PV) grid-connected power system with partially unavailable states. The unavailable states are estimated by a state observer. The output feedback control scheme combines the adaptive dynamic programming (ADP) approach with the dynamic surface control (DSC) technique based on the backstepping design framework, in which the DSC technique can simplify the computation. By constructing an observer-critic-actor architecture, NNs are utilized via reinforcement learning (RL) to approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equation, such that the difficulty of solving the HJB equation is overcome. By integrating the hyperbolic tangent function with a first-order auxiliary system, the adverse influence of the saturated links is removed. All the variables of the closed-loop PV power system are proved to be semi-globally uniformly ultimately bounded (SGUUB) by the Lyapunov stability theory. The simulation and comparative results show the feasibility and superiority of the presented control scheme.
Keywords:
dynamic surface control
output feedback optimal control
single-phase PV grid-connected power systems
state observer
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