References

The key concept of this chapter — model predictive control (MPC), aka receding horizon control (RHC) — has been described in a number of dedicated monographs and textbooks. Particularly recommendable are [1] and [2]. They are not only reasonably up-to-date, written by leaders in the field, but they are also available online for free.

Some updates as well as additional tutorials are in [3], which seems to be available to CTU students through the institutional access.

There is no shortage of lecture notes and slides as well. Particularly recommendable are the course slides [4], and [5].

Extensions towards nonlinear systems (nonlinear model predictive control, NMPC) are described in [6], which is also available to CTU students through the institutional access. Alternatively, concise introductions are [7] and [8, Ch. 15].

Since MPC essentially boils down to solving optimization problems in real time on some industrial device, the topic of embedded optimization is important. An overview is given in [9]. Although some new solvers appeared since its publication, the practical considerations highlighted in the paper are still valid.

Back to top

References

[1]
J. B. Rawlings, D. Q. Mayne, and M. M. Diehl, Model Predictive Control: Theory, Computation, and Design, 2nd ed. Madison, Wisconsin: Nob Hill Publishing, LLC, 2017. Available: http://www.nobhillpublishing.com/mpc-paperback/index-mpc.html
[2]
F. Borrelli, A. Bemporad, and M. Morari, Predictive Control for Linear and Hybrid Systems. Cambridge, New York: Cambridge University Press, 2017. Available: http://cse.lab.imtlucca.it/~bemporad/publications/papers/BBMbook.pdf
[3]
S. V. Raković and W. S. Levine, Eds., Handbook of Model Predictive Control. in Control Engineering. Birkhäuser Basel, 2019. Accessed: Mar. 06, 2019. [Online]. Available: https://www.springer.com/us/book/9783319774886
[4]
A. Bemporad, “Model predictive control.” May 2021. Available: http://cse.lab.imtlucca.it/~bemporad/teaching/mpc/imt/1-linear_mpc.pdf
[5]
S. Boyd, “Model Predictive Control (EE364b - Convex Optimization II.).” Stanford University. Accessed: Feb. 25, 2019. [Online]. Available: https://stanford.edu/class/ee364b/lectures/mpc_slides.pdf
[6]
L. Grüne and J. Pannek, Nonlinear Model Predictive Control: Theory and Algorithms, 2nd ed. in Communications and Control Engineering. Cham: Springer, 2017. Available: https://doi.org/10.1007/978-3-319-46024-6
[7]
S. Gros, M. Zanon, R. Quirynen, A. Bemporad, and M. Diehl, “From linear to nonlinear MPC: Bridging the gap via the real-time iteration,” International Journal of Control, vol. 93, no. 1, pp. 62–80, Jan. 2020, doi: 10.1080/00207179.2016.1222553.
[8]
S. Gros and M. Diehl, “Numerical Optimal Control (draft).” KU Leuven, May 2020. Available: https://www.syscop.de/teaching/ss2017/numerical-optimal-control
[9]
H. J. Ferreau et al., “Embedded Optimization Methods for Industrial Automatic Control,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 13194–13209, Jul. 2017, doi: 10.1016/j.ifacol.2017.08.1946.