基于RBF神经网络优化的PID控制算法研究

Research on PID control algorithm based on RBF neural network optimization

为了更好地解决常规PID控制精度差、无自适应性、跟随性能差等问题,将RBF神经网络与常规PID控制算法结合起来,可以实现动态辨识,利用神经网络的学习能力,可以根据控制环境在线修正PID控制的比例、积分、微分参数,使其更加符合调节需求,从而能够提升系统的实时性以及适应性,通过加入阶跃信号和正弦信号两种不同的信号,基于Matlab软件中的Simulink环境对控制系统进行仿真,验证基于RBF神经网络PID控制算法的控制性能。通过控制系统仿真结果得出结论:基于RBF神经网络PID控制算法具有响应速度快、超调小和跟随性能好、无静态偏差等优点,其控制效果明显优于常规PID控制算法。

In order to better solve the problems of poor precision,no adaptability and poor following performance of conventional PID control,the combination of RBF neural network and conventional PID control algorithm can realize dynamic identification.Using the learning ability of neural network,the proportional,integral and differential parameters of PID control can be modified online according to the control environment to make it more in line with the adjustment requirements.In this way,the real-time performance and adaptability of the system can be improved.By adding two different signals,step signal and sine signal,the control system is simulated based on Simulink environment in Matlab software,and the control performance of PID control algorithm based on RBF neural network is verified.Through the simulation results of the control system,it is concluded that the PID control algorithm based on RBF neural network has the advantages of fast response speed,small overshoot,good following performance and no static deviation,and its control effect is obviously superior to the conventional PID control algorithm.