Abstract
This chapter explains the synthesis of explicit MPC feedback laws that allow for real-time implementation on hardware with limited computational and storage properties. Four methods are introduced. The first one replaces the potentially complex explicit MPC controller by a simpler feedback law by exploiting the geometry of explicit solutions. The second method reduces the storage footprint of explicit MPC by a complete elimination of critical regions, replaced by a direct evaluation of optimality conditions. The common denominator of both methods is that they preserve optimality while considerably reducing the complexity. The third method trades lower complexity for suboptimality while simultaneously minimizing the performance loss. Finally, a method for designing stabilizing explicit MPC controllers for control of nonlinear systems is introduced.
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Notes
- 1.
If \(G_{\mathcal{A}}\) does not have a full row rank, it is always possible to identify a subset of \(\mathcal{A}\) such that all rows of \(G_{\mathcal{A}}\) are linearly independent, see, e.g., [37].
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Acknowledgements
M. Kvasnica, J. Holaza, and P. Bakarac gratefully acknowledge the contribution of the Slovak Research and Development Agency under the project APVV 15-0007.
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Kvasnica, M., Jones, C.N., Pejcic, I., Holaza, J., Korda, M., Bakaráč, P. (2019). Real-Time Implementation of Explicit Model Predictive Control. In: Raković, S., Levine, W. (eds) Handbook of Model Predictive Control. Control Engineering. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-77489-3_17
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