考虑摩擦伺服系统的补偿算法研究

Research on Compensation Algorithm for Servo System with Friction

针对摩擦非线性影响伺服系统动静态性能的问题,在典型伺服系统模型中引入LuGre摩擦模型,介绍了一种应用自适应神经网络补偿摩擦的控制算法。在负载转矩未知、模型包含不确定项、系统参数时变的情况下,利用神经网络对非线性项进行逼近,同时引入自适应的思想,利用反步法设计自适应控制器在线补偿神经逼近系统的估计误差。此外,通过Lyapunov稳定定理对控制系统进行分析,证明整体系统是渐进稳定的。仿真结果表明:该补偿方法能对伺服系统中摩擦进行有效抑制,保证系统的跟踪性能,并在负载扰动和系统参数时变情况下仍具有较强的鲁棒性。

As a main nonlinearity in the servo system,friction cases a bad influence on the dynamic and static characters of control system. A typical servo system model with the LuGre friction was introduced. Based on the adaptive neural network algorithm,a friction compensation method was developed. The adaptive neural network system was used to approximate the nonlinear sections including unknown load torque,variable system parameters and uncertain factors. Besides,an adaptive controller was designed based on the back-stepping method,which effectively decreases the estimation error of the neural approximation system. Finally,it＇s reasonably proved by using Lyapunov function that the whole system is asymptotically stable. Simulation results show that the adaptive neural network controller not only restrains the friction influence and improves the tracking performance of the servo system,but also has strong robustness in case of the uncertain load and system parameters change.