仿人模糊神经网络控制器的设计和实现

应用仿人智能鲁棒性高、能对付难控对象的控制特点，结合模糊RBF 神经网络控制技
术，提出仿人模糊神经网络控制方法，对PID 控制器参数进行优化调节。该方法采用仿人智能的多模态控制思想，对PID 控制器的模式进行选择。对非线性系统控制的仿真结果表明,加入仿人控制后，系统从响应速度和上升时间上均有明显提高，具有良好的控制效果。
关键词：仿人智能；模糊控制；RBF 神经网络； PID 控制；仿真
The Design And Implementation of Human-Simulated and Fuzzy Neural Network ControllerZHANG Hong (Department of Information and Control;Xi’an University of Post and Telecommunications;Xi’an 710061;china) Abstract: Human-Simulated Fuzzy Neural Network Control method is proposed, which applies with the performance of the high robust and the performance of dealing with the objects with dificulty of controlling , combines with fuzzy RBF Neural Network control technology, optimize the parameters of PID controller. The method uses multi-mode control thought of Human-Simulated intelligence, select the mode of PID controller .Simulation results in nonlinear system shows that the control performance of Human-Simulated fuzzy RBF Neural Network is better than the fuzzy RBF Neural Network .
Key words:Human-Simulated intelligence; Fuzzy control; RBF Neural Network ;PID
control;Simulation