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Robust Model Predictive Control for Nonlinear Systems using Linear Matrix Inequality

Khaksarpour, Reza | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42591 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Haeri, Mohammad
  7. Abstract:
  8. The constrained nonlinear systems with large operating regions have attracted great attention due to their correspondence with the most practical systems. There are several tools such as gain scheduling and Nonlinear Model Predictive Control (NMPC) to control them. Gain scheduling, with ability to provide stability guarantees between the estimated stability regions overlapping each other and to cover a large space of the allowable operating range of the system, is an attractive practical approach to control the systems with large operating regions. But this strategy do not account for constraints explicitly by online optimization. On the contrary, NMPC handles constraints on the manipulated and controlled variables explicitly which leads it be a powerful control strategy for different industrial plants. Since NMPC generally involves a nonlinear non-convex optimization problem, it has a great computationally burden and, furthermore, it cannot estimate explicitly the region of stability. The idea of composing NMPC and gain scheduling to use the merits of both of them, therefore, can be a proper scheme to control the mentioned systems. The proposed algorithm is a computationally efficient scheduled robust constrained MPC which can be useful to control the locally Lipschitz nonlinear systems which, in several regions of operating range, can be presented by a linear part perturbed by an additive nonlinearity term. In addition to have smaller computationally burden and to present a larger allowable set of initial conditions, the proposed algorithm shows proper performance, even in presence of disturbances or possible uncertainties, which is illustrated by simulated examples
  9. Keywords:
  10. Predictive Control ; Nonlinear Systems ; Linear Matrix Inequality (LMI) ; Gain Scheduling ; Unstructured Uncertainty

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