Е-Публикации
Технически университет - София

Abstract: The plants in modern industry are difficult to model due to their nonlinearity, inertia, and changes with operation modes, points and environment. Models are important in design and optimisation of sophisticated control algorithms required for ensuring high quality of the products and efficient energy and materials consumption. The present research focuses on the development of a novel methodology for Takagi-Sugeno-Kang (TSK) modelling and model validation of complex nonlinear plants. It is based on expert knowledge and parameter optimisation using genetic algorithms (GA) with experimental data and simulations. A TSK plant, inverse plant and predictive plant models are derived for the temperature in a laboratory fruit dryer. They are applied in PID-based nonlinear Smith predictor, predictive and linearising control which can be easily implemented in programmable logic controllers for broad real time applications. Simulations show increased closed loop system dynamic accuracy and energy efficiency.

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