Improved sliding mode control of direct-drive electro-hydrostatic actuator based on adaptive neural network

Aiming at the problem that various parameter uncertainties and disturbances degrade the tracking control performance in the direct-drive electro-hydrostatic actuator, an improved sliding mode control based on adaptive neural network is proposed in this paper. First, a new sliding mode reaching law is designed. On the basis of the exponential reaching law, a variable gain term based on system state variables, a piecewise function with power terms of the sliding surface and a new switching function are introduced, which improves the response speed, enhances the anti-disturbance capability and suppresses chattering. Secondly, a new adaptive radial basis function neural network observer based on the minimum learning parameter is designed. The outputs of the high-gain observer are taken as the inputs of the radial basis function neural network. An adaptive learning rate dynamically varying with the observation error is developed and incorporated into the weight updating law of the neural network, which improves the real-time nonlinear approximation accuracy for unknown disturbances. The stability was proved through the Lyapunov function. The results demonstrate that the proposed control method outperforms the other control methods in terms of both response speed and control accuracy.