Adaptive neural network dynamic surface optimal saturation control for single-phase grid-connected photovoltaic systems

An adaptive neural network (NN) based optimal saturation control scheme is investigated for single-phase grid-connected photovoltaic (PV) systems by incorporating dynamic surface control (DSC) and adaptive dynamic programming (ADP) based on the backstepping control design framework. For each backstepping step, a critic-actor architecture is constructed via reinforcement learning (RL), and the PV system is optimized according to the cost function in the architecture. Due to the nonlinearity, it is difficult to solve the Hamilton-Jacobi-Bellman (HJB) equation. The neural networks (NNs) are employed to approximate the solution of the HJB equation such that the optimal virtual control and the actual controller are obtained. By considering control input symmetric saturation nonlinearity link, constraints on pulse width modulation (PWM) are ensured. On this basis, the combination of backstepping control design and dynamic surface technique is used to overcome the shortcomings of "differential explosion" and simplify calculations. Based on the Lyapunov method, the stability analysis proves that all signals of the closed-loop PV systems are semiglobally uniformly ultimately bounded (SGUUB). Simulation experiments and comparative results are given to verify the efficacy of the studied control strategy. This paper proposes an adaptive neural network-based optimal saturation control scheme for single-phase grid-connected photovoltaic systems. The scheme incorporates dynamic surface control and adaptive dynamic programming based on the backstepping control design framework. The designed controller guarantees that all signals of the closed-loop system are semi-globally uniformly ultimately bounded. image