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针对高精度电液飞行仿真转台具有高度非线性、参数不确定和不确定非线性等特点,提出了一种基于RBF(Radial Basis Function)神经网络的非线性积分滑模鲁棒控制方法.采用自适应RBF神经网络对该系统存在的参数不确定性和不确定非线性进行补偿,从而降低滑模控制器对切换项的增益的需求,进而减小系统抖振幅值.积分滑模面的设计能消除外部干扰对系统带来的稳态误差.根据积分滑模变结构控制器的特点,将控制律分为等效控制律和到达控制律.等效控制律使系统运动于滑模面附近,到达控制律可使处于状态空间内任意初始位置的系统趋近于滑模面,并进一步通过Lyapunov方法证明了系统的渐近稳定性.实验结果表明,所提出的非线性控制器不仅能满足电液转台的高精度跟踪性能的要求,且对参数不确定性和不确定非线性具有一定的鲁棒性.
Aiming at the characteristics of high precision electro-hydraulic flight simulation turntable, such as highly nonlinear, uncertain parameters and uncertain nonlinearity, a nonlinear integral sliding mode robust control method based on Radial Basis Function (RBF) neural network is proposed. RBF neural network is adapted to compensate for the uncertainties and uncertain nonlinearities of the system, so that the sliding mode controller can reduce the gain of switching items and reduce the amplitude of chattering. The steady-state error caused by external disturbance is eliminated.According to the characteristics of integral sliding mode variable structure controller, the control law is divided into equivalent control law and arrival control law.The equivalent control law makes the system move near the sliding mode surface, The arrival control law can make the system in any initial position in the state space approach the sliding surface and further prove the asymptotic stability of the system by the Lyapunov method.The experimental results show that the proposed nonlinear controller can not only satisfy the requirements of electricity Liquid turret high-precision tracking performance requirements, and the parameters of uncertainty and uncertainty of non-linear with a certain degree of robustness.