Integral sliding mode nonlinear controller of electrical-hydraulic flight simulator based on neural network

For the feature that high-accuracy electrical-hydraulic flight simulator (EHFS) is highly nonlinear and contains parametric uncertainties and uncertain nonlinearities, an integral sliding mode nonlinear robust controller based on radial basis function (RBF) neural network was proposed. The adaptive RBF neural network was adopted to eliminate the effect of parametric uncertainties and uncertain nonlinearities. By reducing the gain of switching function in sliding mode controller, chattering phenomenon could be minimized significantly. The steady state error from external disturbances could be eliminated by integral sliding control law, which was divided into an equivalent control law and a hitting control law. Equivalent control law was designed to keep the system sliding along the sliding surface. Hitting control law was applied to drive the representation point of the state space onto the sliding surface. The globally asymptotic stability of developed controller was proven via Lyapunov analysis. Comparative experimental results demonstrate the effectiveness of the proposed algorithm.