Imperial College London > Talks@ee.imperial > Control and Power Seminars > Model Predictive Control of linear Systems with disturbances - control parameterizations and their tradeoffs

Model Predictive Control of linear Systems with disturbances - control parameterizations and their tradeoffs

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If you have a question about this talk, please contact Eric C Kerrigan.

Model Predictive Control (MPC) has been an active area of research for controling dynamical systems with state and control constraints. This talk focuses on linear systems with bounded additive disturbances – a class of systems that has received reasonable attention in the literature. The first part of the talk provides a brief review of some of the popular MPC control parameterizations and their properties. It will be seen that the parameterizations have several tradeoffs in terms of the computational load, performance and the associated domain of attraction. The second part of the talk introduces a control parameterization that aims to reduce the online computational load by using fewer variables than standard. The use of fewer variables is a common approach for lower computational load. However, issues such as recursive feasibility, robustness and the choice of variables remain open research problems. The proposed control parameterization is an attempt to answer some of these questions.

This talk is part of the Control and Power Seminars series.

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