基于命令滤波和神经网络的电液伺服系统有限时间反步控制OA

To improve the tracking control performance of electro-hydraulic servo systems,considering the lumped uncertainties,including parameter uncertainty,unmodeled dynamics,and unknown disturbances,a finite-time backstepping control method based on command filter and neural network is proposed.A Levant differentiator is used as the command filter to obtain the derivative of the differential signal between the virtual input variable and the virtual control law,which not only avoids the"explosion of complexity"in standard backstepping control,but also estimates unmatched uncertainty through reconstruction;Compared with traditional neural network-based backstepping control that requires multiple neural networks,this method only uses one neural network to approximate the matched uncertainty,avoiding the complexity and fragility of controllers caused by multiple neural networks.By introducing piecewise feedback functions composed of a fractional exponential power function and a polynomial function to accelerate convergence,the system achieves finite time stability while avoiding singularity problems.Finally,an experimental platform is established to conduct comparative experiments,and the effectiveness and superiority of the proposed new method are verified.