| Summary: | This thesis deals with the industrial application of sliding mode controllers. Sliding mode controllers based on both linear models and nonlinear-models are considered. Special attention is paid to the nonlinear modelling of the systems for sliding mode controller design. The possibility of using neural networks for model generation is explored. Novel schemes for uncertainty bounds estimation are introduced and subsequently used for robust sliding mode controller design. Later, a novel approach for sliding mode based parameter estimation for a nonlinear model with known structure but unknown parameters is introduced. This parameter estimation scheme is integrated with sliding mode controller design to provide an overall controller design framework. The stability of these schemes is proven through quadratic stability concepts. The sliding mode controller design frameworks mentioned above are verified and tested on challenging industrial examples. The temperature control of a high temperature multiburner industrial furnace is a highly coupled and extremely nonlinear problem. A multiburner furnace nonlinear simulation facility is established and used for linear identification and subsequently linear model based sliding mode controller testing. For comparison purposes a two degree of freedom H controller is also designed and tested. Then a nonlinear model based controller is tested on a single burner furnace simulation. Idle speed control of an automobile engine is an extremely difficult control problem characterised with severe nonlinearities, gross disturbances and huge time delays. A sliding mode controller is designed for this problem and successfully implemented on a test rig. Later on, a nonlinear model based sliding mode controller is designed for the same problem and successfully tested.
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