Adaptive sliding mode control with neural network based hybrid models

In the sliding mode control with a boundary layer approach, the thickness of the boundary layer required to completely eliminate the control input chattering depends on the magnitude of the switching gain used. A controller with higher switching gain produces higher amplitude of chattering and thus needs to use a thicker boundary layer. On the other hand, the value of the switching gain used depends on the bounds of system uncertainties. Hence, a system with large uncertainties needs to use a thicker boundary layer to eliminate chattering. However, the control system is actually changing to a system without sliding mode if we continuously increase the boundary layer thickness in order to cater for systems with large uncertainties. To solve this problem, it is proposed here to use neural networks to model the unknown parts of the system nonlinear functions such that we can obtain a better description of the plant, and hence enable a lower switching gain to be used. The network outputs were combined with the available knowledge, which formed the so-called hybrid models, to approximate the actual nonlinear functions. The controller performance is demonstrated through simulation studies on a two-tank level control system and a continuous stirred tank reactor system. The results showed that the incorporation of networks has enabled a lower switching gain to be used, and thus the chattering in the control inputs can be eliminated even though with a thin boundary layer. (C) 2003 Elsevier Ltd. All rights reserved.