Adaptive control of electric drives using sliding-mode learning neural networks

New sliding mode control theory-based method for on-line learning in multilayer neural controllers as applied to the speed control of electric drives is presented. The proposed algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives.