Abstract
This paper presents a Nonlinear Model Predictive Control (NMPC) algorithm that uses hard variable constraints to allow for control objective prioritization. Traditional prioritized objective approaches can require the solution of a complex mixed-integer program. The formulation presented in this work relies on the feasibility and solution of a relatively small logical sequence of purely continuous nonlinear programs (NLP). The proposed solution method for accomodation of discrete control objectives is equivalent to solution of the overall mixed-integer nonlinear programming problem. The performance of the algorithm is demonstrated on a simulated multivariable network of air pressure tanks.
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Long, C.E., Gatzke, E.P. (2007). Hard Constraints for Prioritized Objective Nonlinear MPC. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_17
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DOI: https://doi.org/10.1007/978-3-540-72699-9_17
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