An adaptive dynamic special global sliding mode controller that is based on proportional integral derivative (PID) sliding surface using radial basis function (RBF) neural network (NN) for a three- phase active power filter (APF) was presented in this paper. To overcome the problems associated with the schemes of the conventional sliding mode control, a global PID sliding manifold is introduced to realize the whole process of robustness and inhibition of the steady state error, accelerating the system response meanwhile. In addition, the nested dynamic sliding mode controller can reduce the influence of chattering that may lead to malfunction of the insulated gate bipolar transistor (IGBT) caused by sign function in the control law, achieving a better property. Moreover, owing to the parameter uncertainties and the external disturbances, a RBF neural estimator is added to eliminate the chattering phenomenon that further optimizes the performance of the system. Eventually, simulation studies in the MATLAB/SimPower Systems Toolbox verify the outstanding performance of the designed RBFNN dynamic global PID sliding mode controller in three different conditions, and some comparisons are made at the same time to demonstrate the excellent properties of the raised control method.

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An adaptive global sliding mode fuzzy control using radial basis function (RBF) neural network (NN) based on backstepping technique is presented for a micro electromechanical systems (MEMS) gyroscope. The proportion integral differential (PID) sliding surface has the capacity of restraining the steadystate error. Meanwhile, we take advantage of the global sliding mode manifold to overcome shortcomings of the conventional sliding mode controller, obtaining the fast response and overall robustness. Furthermore, faced with the unknown dynamic characteristic of the MEMS system, an RBF neural approximator is employed to estimate it. Besides, a fuzzy controller is put forward to suppress the chattering phenomenon caused by the sliding mode controller. The globally asymptotic stability of the closed loop system is guaranteed by the selected adaptive laws and Lyapunov theory. The simulation results demonstrate satisfactory effects of the proposed advanced controller. The comparison studies verify the better properties of the suggested control approach. INDEX TERMS Micro electromechanical systems gyroscope, PID global sliding mode control, RBF neural network control, backstepping control, fuzzy control.

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