Abstract
A suboptimal approach to distributed robust MPC for uncertain systems consisting of polytopic subsystems with coupled dynamics subject to both state and input constraints is proposed. The robustness is defined in terms of the optimization of a cost function accumulated over the uncertainty and satisfying state constraints for a finite subset of uncertainties. The approach reformulates the original centralized robust MPC problem into a quadratic programming problem, which is solved by distributed iterations of the dual accelerated gradient method. A stopping condition is used that allows the iterations to stop when the desired performance, stability, and feasibility can be guaranteed. This allows for the approach to be used in an embedded robust MPC implementation. The developed method is illustrated on a simulation example of an uncertain system consisting of two interconnected polytopic subsystems.
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Acknowledgments
This work was financed by the National Science Fund of the Ministry of Education and Science of Republic of Bulgaria, contract No. DRila 01/12 and the Partenariats Hubert Curien (PHC) Programme Rila of the French Government, contract No. 29401YJ “Robust distributed predictive control of complex systems.”
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Grancharova, A., Olaru, S. (2015). Distributed Robust Model Predictive Control of Interconnected Polytopic Systems. In: Olaru, S., Grancharova, A., Lobo Pereira, F. (eds) Developments in Model-Based Optimization and Control. Lecture Notes in Control and Information Sciences, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-26687-9_4
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DOI: https://doi.org/10.1007/978-3-319-26687-9_4
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