Abstract
Extension of the efficient Model Predictive Control (MPC) algorithm, which uses fuzzy approximations of nonlinear models, with mechanisms of disturbance measurement utilization is proposed. Two methods of disturbance measurement utilization are considered. The first method utilizes a fuzzy model of disturbance influence on the control plant, whereas the second one – a nonlinear model used to obtain the free response. In both methods only the free response generated during the prediction calculation is influenced. Therefore, the prediction has such a form that the MPC algorithm remains to be numerically efficient. Only a quadratic optimization problem must be solved at each iteration in order to derive the control signal. The proposed methods of disturbance measurement utilization can significantly improve control performance offered by the algorithm what is demonstrated in the example control system of a nonlinear chemical CSTR reactor with inverse response.
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Marusak, P.M. (2013). Disturbance Measurement Utilization in the Efficient MPC Algorithm with Fuzzy Approximations of Nonlinear Models. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_32
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DOI: https://doi.org/10.1007/978-3-642-37213-1_32
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