Abstract:
In this thesis, a direct approach to improve the control of highly nonlinear, strongly coupled boiler-turbine systems that are commonly found in the power generation environment is introduced. Following the direct approach, more generalized concepts of controlling polynomial systems, a class of nonlinear systems that is superior to linear systems in its adaptability to real life systems in terms of system modelling or approximation of other nonlinearities, is discussed in detail. The motivation for this research stems from its usefulness for a variety of power generating facilities used around the world. In particular, the implementation of an online model predictive control scheme based on evolutionary computation will be introduced, including an extension to a switching control regime to further increase the overall performance. The discussions on polynomial system control is based on the lack of a natural extension of linear control strategies to polynomial systems, a difficult problem that cannot be directly addressed by standard convex optimization tools like semidefinite programming. However, new methodologies will be introduced for a variety of polynomial control problems, including H¥ control for systems with and without polytropic or norm-bounded uncertainties, which lead to an overall less conservative control design. The discussion will include robust H¥ control procedures for near real-world control problems that are subjects to polytropic and norm-bounded uncertainties for systems with the state and output feedback. Finally, to demonstrate the effectiveness and advantages of the proposed design methodologies in this thesis, numerical examples are given in each chapter. The simulation results show that the proposed design methodologies can achieve the prescribed performance requirements.
