基于神经网络的非线性系统多步预测控制

针对离散非线性系统，利用非线性激励函数的局部线性表示，提出一种可用于非线性过程的神经网络多步预测控制方法，并给出了控制律的收敛性分析．该方法将非线性系统处理成简单的线性和非线性两部分，对复杂的非线性多步预测方程给出了直观而有效的线性形式，并用线性预测控制方法求得控制律，避免了复杂的非线性优化求解．仿真结果表明了该算法的有效性．

Neural network based multi-step predictive control for nonlinear systems

A neural network model-based multi-step predictive control algorithm with its convergence analysis for nonlinear discrete systems is presented. The structure of this nonlinear system is separated into a simple linear part and a nonlinear part by using the local linearization of nonlinear activation functions. The proposed algorithm gives a direct and effective multi-step predicting method. It uses simple linear predictive control methods to get the control law and avoids the complicated nonlinear optimization. Simulation results show the efficiency of this method.