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

Hongyang Zhang, Tiechao Wang
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Abstract

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.
单相并网光伏系统的自适应神经网络动态表面优化饱和控制
在反步进控制设计框架的基础上,结合动态表面控制(DSC)和自适应动态编程(ADP),研究了一种基于自适应神经网络(NN)的单相并网光伏(PV)系统最优饱和控制方案。对于每个反步进步骤,通过强化学习(RL)构建了一个批判-代理架构,并根据架构中的成本函数对光伏系统进行优化。由于非线性,很难求解汉密尔顿-雅各比-贝尔曼(HJB)方程。采用神经网络(NN)来近似求解 HJB 方程,从而获得最优虚拟控制和实际控制器。通过考虑控制输入对称饱和非线性环节,确保了对脉宽调制(PWM)的约束。在此基础上,结合反步态控制设计和动态曲面技术,克服了 "微分爆炸 "的缺点,简化了计算。基于 Lyapunov 方法的稳定性分析证明,闭环光伏系统的所有信号均为半全局均匀终极约束(SGUUB)。仿真实验和比较结果验证了所研究控制策略的有效性。
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