In this dissertation, we present a methodology of combining an improved Gaussian mixture models (GMM) with local linear models (LLM) for dynamical system identification and robust predictive model control. In order to understand the advantage of the mixture model, its structure and training are discussed in detail. A growing self-organizing map is utilized to improve the random initialization of mixture models, which makes the GMM convergence more stable. To increase local modeling capability and decrease modeling error, local linear models are trained based on GMM as one-step predictors. Following the local modeling approach, a series of controllers are designed to realize a tracking application, among which the optimal robust control shows better robustness over other controllers. Five application systems with different dynamics are simulated in order to verify the modeling and control capability of the improved Gaussian mixture model. Through experiments and comparison with self-organizing maps, radial basis functions, and other methodologies, it is shown that the improved GMM-LLM approach is a more flexible modeling approach with higher computation efficiency than its competitors. The Gaussian model algorithm shares with the self-organizing maps the ease of robust controllers design.
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