摘要
凸轮机构由于其优良的工作性能而被广泛用于高精度往复运动系统中。但由于它的变转动惯量、变负载力矩、间隙等非线性动力学特性 ,给含有凸轮机构的机电系统稳速控制带来了很大难度。在诸多影响稳速控制精度的因素中 ,负载扰动力矩的影响是最主要的。本文利用神经网络逼近非线性函数的能力和自适应、自学习的特点建立了实际系统的负载扰动力矩神经网络模型 ,并基于全补偿原则设计了补偿环节 ,实现了对扰动力矩的动态补偿。实验结果表明这种方法可以有效解决凸轮系统中固有的周期性扰动对稳速控制的不利影响 ,切实提高稳速控制精度 ,具有一定的实用价值。
Cam mechanism is widely used in interactive movement systems because of its excellent movement characteristics. But the changing torque and inertia moment make the speed stabilizing control of the systems with cam mechanism very difficult. The changing load torque is the primary one in all the factors that influence the speed control accuracy. This thesis makes full use of the approximating to nonlinear functions capability of neural networks and establishes the load torque neural networks model of the system with cam mechanism. Based on the whole compensating theory, a speed stabilizing control system with neural networks compensatory loop is designed. Experiment results convince that the method works very well and can be used in other control systems with changing load torque.
出处
《光学精密工程》
EI
CAS
CSCD
2000年第2期165-168,共4页
Optics and Precision Engineering
关键词
凸轮机械
神经网络
稳速控制
cam mechanism
neural networks
speed control
