基于神经网络的仿真转台控制系统

在转台存在偏载、摩擦等不确定负载干扰的情况下,用神经网络与PID(Proportional-Integral-Differential)控制相结合的方法,设计了适应负载变化的转台控制系统.分析了基于BP(Back Propagation)神经网络的自适应PID控制器的基本原理,建立了转台位置控制系统的数学模型,并对控制系统进行仿真分析和实验验证,通过与传统PID控制的对比实验与仿真表明:所设计系统由于有自学习能力,能动态调整PID参数,使系统表现出良好的抗干扰能力和跟踪性能,证明了所设计系统的有效性.该算法结构简单,PID初始参数调整方便,易于在转台实时控制系统中应用.

Simulating turntable control system with neural network

To solve the turntable uncertain partial load and friction disturbance, a turntable control system was designed with neural-proportion-integral-differential (PID) theory. Because of the learning capacity of neural network, the control system showed adaptive capacity to the load disturbance. The basic theory of a self-adaptive PID controller based on back propagation (BP) neural network was described, The mathematic model of the turntable position control system was set up. A thorough analysis on the system was given by simulation and experiments. The simulation and experiment results prove that the turntable with neural-PID controller shows good track performance and capacity against the load disturbance, but the traditional PID controller hasn’t. The neural-PID system can regulate the PID parameters dynamically by self-learning to fit for the load changes and makethe PID parameters regulation become easier. The controller has a simple structure and can be easily realized in engineering. The results show the effectiveness of the control algorithm.