Neural-Network-Based Sliding Mode Control for Missile Electro-Hydraulic Servo Mechanism
A method investigating a Gaussian radial-basis-function neural network (GRBFNN) with sliding mode control (SMC) for missile electro-hydraulic servo mechanism is presented. Since the dynamics of the system are highly nonlinear and have large extent of model uncertainties, such as big changes in parameters and external disturbance, firstly, SMC is introduced. Since the accurate equivalent control is difficult to reach, a Gaussian radial basis function neural network is utilized. By adjusting the weight on-line, a neural-network-based SMC is developed to estimate the equivalent control of SMC control system. Then the switching control is appended to guarantee the stability of the proposed controller, and a set of fuzzy control rules are used to attenuate chattering phenomenon of the switching control. We apply the control method to the missile electro-hydraulic servo mechanism. Simulation results verify the validity of the proposed approach.
KeywordsSlide Mode Control Radial Basis Function Neural Network Switching Control Servo Mechanism Variable Structure Control
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