基于递归神经网络的伺服系统自适应反步控制

Adaptive-Backstepping Control for Servo System Based on Recurrent-Neural-Network

针对伺服系统的系统参数摄动和非线性动态摩擦补偿问题,提出基于递归神经网络(RNN)的自适应反步控制(RNABC)系统设计方法。RNABC系统由反步控制器和鲁棒控制器组成,反步控制器包含RNN不确定观测器,鲁棒控制器则用来消除由于引入不确定观测器而带来的逼近误差。由于自适应反步控制的自适应律源于Lyapunoy函数的,因此系统的稳定性得到了保证。仿真结果表明,对于系统参数摄动和非线性摩擦干扰RNABC能使伺服系统具有很好的跟踪性能。

For the problem of parameter variation and nonlinear dynamic friction compensation, a recurrent-neural-network （RNN）-based adaptive-backstepping control （RNABC） for servo system was proposed. The RNABC system is comprised of a backstepping controller and a robust controller. The backstepping controller containing an RNN uncertainty observer is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the uncertainty observer. The adaptation laws of the adaptive-backstepping approach are derived in the sense of the Lyapunov function, thus, the stability of the system can be guaranteed. Simulation results verify that the proposed RNABC can achieve favorable tracking performance for servo system, even regard to parameter variations and friction disturbance.