高精度伺服系统神经网络自适应控制研究

经典的、基于对象模型的PI控制方法简单、易于实现,但对于一些负载扰动和模型参数的变化,往往不能起到很好的抑制作用。针对上述问题,提出了一种神经网络自适应PI的控制方法,利用负载干扰观测器和神经网络自适应地调整PI控制器的参数,从而来有效地减少负载的干扰和模型参数的变化对系统造成的影响,提高了系统的鲁棒性。仿真结果表明了该方法的有效性。

Self-tuning Control of High Precision Servo-system Using Neural Network

The traditional PI control method based on a plant model is simple, and easy to realize. But, it has not a good restraining effect on the varieties of load disturbance and model parameters. To resolve the problems, a self-tuning PI control method was proposed using a neural network. The PI parameters are adjusted by use of the load disturbance observer and neural network identifier, so the influence caused by the load disturbance and model parameters varieties are weakened, and the self-tuning PI control method improves the system robustness. The simulation result shows that the method is valid.