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针对存在未知非线性电液速度伺服系统的跟踪控制问题,结合神经网络与滑模变结构控制理论,提出一种自适应神经滑模控制方案.标称控制律用来控制标称系统,而对系统中不确定部分采用基于神经网络的滑模补偿控制律控制.RBF神经网络用来实现对未知非线性系统的建模,Lyapunov稳定性理论实现网络权值的自适应修正规则.利用对称型Sigmoid连续函数以平滑不连续控制,达到削弱高频抖振的目的.仿真结果显示,该方案可以减小跟踪误差、增强系统的鲁棒性和削弱控制信号中的高频抖振.
Aiming at the problem of tracking control with unknown nonlinear electro-hydraulic servo system, an adaptive neural sliding mode control scheme is proposed based on neural network and sliding mode variable structure control theory. The nominal control law is used to control the nominal system, The uncertain part of the system is controlled by sliding mode compensation control law based on neural network.The RBF neural network is used to realize the modeling of unknown nonlinear systems and Lyapunov stability theory to adaptively correct the network weights.Using symmetric Sigmoid The continuous function achieves the purpose of weakening high frequency chattering by smooth discontinuous control.The simulation results show that this scheme can reduce the tracking error, enhance the system robustness and weaken the high frequency chattering in the control signal.