一种非线性系统神经网络直接控制器的设计

提出了一种使用神经网络作为非线性对象直接控制器的设计方法 ,该控制器由一个常规控制器和一个神经网络控制器组成 .常规控制器对系统给出粗略控制 ,神经网络控制器给出补偿信号来进一步减小系统输出跟踪误差 .该方法对被控非线性对象类型的限制很少 .在该方法中 ,径向基函数 (RBF)神经网络被用来进行训练 ,训练后系统具有较好的稳定性 .仿真结果表明 ,该方法非常有效 ,对非线性系统能取得比较满意的控制效果

DESIGN OF DIRECT CONTROLLER FOR NONLI-NEAR PLANT BASED ON NEURAL NETWORK

In this paper,a direct controller for nonlinear plants using a neural network is presented.The controller is composed of an approximate controller and a neural network auxiliary controller.The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error.This method does not put too much restriction on the type of nonlinear plant to be controlled.In this method,a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for.Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.