Design of a sliding mode control scheme using neural networks

In this paper, a new control strategy is presented that combines sliding mode control theory with a neural network. Sliding mode control theory requires the complete knowledge of the dynamics of the controlled system. However, in practice, this could be a serious limitation on the practical usefulness of sliding mode control theory. A multilayer neural network with a back-propagation learning algorithm is employed to solve this kind of problem. The neural network serves as a compensator without a priori knowledge about the system. The robustness against parameter uncertainty and nonlinearity, and the effectiveness of the proposed algorithm is verified by simulation results.