Dynamic global PID sliding control using neural compensator for active power filter

An adaptive dynamic special global sliding mode controller which 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 problems associated with 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 which may lead to malfunction of the 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 which 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.