A nonlinear sliding mode control design approach based on neural network modelling (original) (raw)
1999, International Journal of Robust and Nonlinear Control
A complete nonlinear framework for the modelling and robust control of nonlinear systems is proposed. The use of neural networks for continuous time modelling to obtain a certain nonlinear canonical form is investigated. The model obtained is used with recently proposed dynamic sliding mode controller design methods. The robustness bounds needed for controller design are determined from modelling errors. A modi"ed version of the backpropagation theorem is also introduced. readily follows. However, this elimination process is not always straightforward. Further, state space equations may not be available. To facilitate wider application of this control method it is necessary to consider estimation methods to generate the GCCF. It will be demonstrated in the paper that FFNN can be used to generate a GCCF form for a given nonlinear system and a dynamic sliding mode controller can be designed for the FFNN-based GCCF model. A generic framework for nonlinear controller design using neural networks and dynamic sliding mode techniques will be proposed. As these techniques are robust in nature, novel ways will be proposed to relate model uncertainties arising from the neural network training to the robustness bounds needed for the controller design.
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