基于神经网络干扰观测器的Terminal滑模控制

针对一类非线性不确定系统,通过构建动态干扰观测器系统,提出一种快速神经网络干扰观测器。根据干扰观测误差在线调节神经网络权值,实现对未知综合干扰的逼近,逼近误差一致最终有界。基于神经网络干扰观测器设计了自适应Terminal滑模控制方案,严格证明了闭环系统状态在有限时间内收敛到零,从而提高了状态的收敛速度。最后,通过一个倒立摆的仿真例子,验证了系统的快速性和神经网络干扰观测器的逼近能力。

Terminal sliding mode control based on neural network disturbance observer

To deal with the disturbances of a kind of uncertain nonlinear systems,a dynamic disturbance observer system is constructed.According to this disturbance observer system,a fast neural network disturbance observer is proposed.Based on the observed disturbance error the observer approaches the unknown synthetic disturbance by on-line adjusting its weights.An adaptive terminal sliding mode control is designed based on neural network disturbance.It is strictly proved that the states of a closed system converge to zero within finite time.Therefore the convergence speeds are improved under this scheme.Simulation results demonstrated the effectiveness of the proposed scheme the capability of the observer to approach disturbance.