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For the problems of parameters disturbance, nonlinear and uncertainty of distribution static compensator (DSTATCOM), this paper studies on sliding mode control based on radial basis function (RBF) network. The fast tracking of DSTATCOM reactive current is achieved by using of RBF neural networks and equivalent sliding mode control. The method has a strong adaptability and robustness for load disturbances...
In this paper, based on a radial basis function (RBF) neural network, a sliding mode control (SMC) strategy for brushless doubly fed machine (BDFM) is presented. The operating principle of BDFM has been introduced. The dynamic model of rotor field oriented and electromagnetic torque for BDFM is expressed. The proposed controller for BDFM eliminates the chattering encountered by most SMC schemes, and...
To solve the control problem of a Stewart platform with unknown dynamics for multiple degree-of-freedom (DOF) active vibration isolation, an adaptive radial basis function neural network(RBFNN) controller is developed. The RBFNN is employed to approximate the unknown dynamics of the system. And an on-line tuning rule for the parameters of the RBFNN is given based on the e1-modification and gradient...
In this paper, a quasi-sliding mode variable structure control algorithm is combined with RBF neural network. So, the strong robustness of the quasi-sliding mode variable structure algorithm and the property of adaptive learning supplying from RBF neural network algorithm are combined. The algorithm is subsequent inducted into the large time-delay system. Simulation results show that the strategy...
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