References and study materials

For each lesson, the lesson diary indicates exactly what to study. The books and papers below are free and openly accessible whenever possible; advanced books are used only for specific sections.

The few items that are not open access are still available to you: Sapienza subscribes to the main publishers, so you can read them from the University network, or from anywhere by signing in with your institutional account — either on the publisher’s site (IEEE Xplore, ScienceDirect, SpringerLink) or through the University proxy of the library system.

Model Predictive Control MPC

Books

  • [Cannon_2016] B. Kouvaritakis, M. Cannon. Model Predictive Control: Classical, Robust and Stochastic. Springer, 2016. Main reference of the course: chapter 2 follows the same route, and the same notation, as the lessons.
  • [Rawlings_2017] J. B. Rawlings, D. Q. Mayne, M. Diehl. Model Predictive Control: Theory, Computation, and Design, 2nd ed. Nob Hill Publishing, 2017. Free PDF from the authors.
  • [Borrelli_2017] F. Borrelli, A. Bemporad, M. Morari. Predictive Control for Linear and Hybrid Systems. Cambridge University Press, 2017. Useful for hybrid (MLD) systems and explicit MPC.
  • [Boyd_2004] S. Boyd, L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. Free PDF from the authors. Used for specific sections: section 4.7 for several objectives at once, Pareto optimality and the weighted sum.

Slides

Papers

  • [Mayne_2000] D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. M. Scokaert. “Constrained model predictive control: Stability and optimality.” Automatica 36(6), 789–814, 2000. Journal.
  • [Bemporad_1999] A. Bemporad, M. Morari. “Control of systems integrating logic, dynamics, and constraints.” Automatica 35(3), 407–427, 1999. Author’s PDF.

Integer programming and logical constraints

Applications

  • [Liberati_2025a] F. Liberati, M. Donsante, A. Tortorelli. “Single intersection MPC traffic signal control in presence of automated vehicles.” IEEE Transactions on Control Systems Technology 33(4), 1432–1446, 2025. IEEE Xplore.
  • [Liberati_2025b] F. Liberati, M. Donsante, C. M. F. Cirino, A. Tortorelli. “Fast task scheduling with model predictive control integrating a priority-based heuristic.” IEEE Access 13, 14499–14515, 2025. Open access.
  • [Kirschen_2019] D. S. Kirschen, G. Strbac. Fundamentals of Power System Economics, 2nd ed. Wiley, 2019. The context of the aggregator case study: how electricity markets are organized, who buys the services the grid operators need, and why flexibility is paid for.
  • [Di Giorgio_2014] A. Di Giorgio, F. Liberati. “Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models.” Applied Energy 128, 119–132, 2014. Journal.
  • [Liberati_2018] F. Liberati, A. Di Giorgio. “Economic model predictive and feedback control of a smart grid prosumer node.” Energies 11(1), 48, 2018. Open access.
  • [Liberati_2019] F. Liberati et al. “Joint model predictive control of electric and heating resources in a smart building.” IEEE Transactions on Industry Applications 55(6), 7015–7027, 2019. IEEE Xplore.

Reinforcement Learning RL

  • [Sutton_Barto] R. S. Sutton, A. G. Barto. Reinforcement Learning: An Introduction, 2nd ed. MIT Press, 2018. Free online.
  • [Bertsekas_2024] D. Bertsekas. A Course in Reinforcement Learning. Athena Scientific. Free PDF. Strong connections with MPC. More books and video lectures on Prof. Bertsekas’ homepage.
  • [Bertsekas_2022] D. Bertsekas. Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control. Athena Scientific, 2022. Free PDF on the author’s homepage.
  • [Reiter_2025] R. Reiter et al. “Synthesis of model predictive control and reinforcement learning: survey and classification.” arXiv:2502.02133.
  • [Gros_2020] S. Gros, M. Zanon. “Data-driven economic NMPC using reinforcement learning.” IEEE Transactions on Automatic Control 65(2), 2020. Preprint on arXiv.
  • [Recht_2019] B. Recht. “A tour of reinforcement learning: the view from continuous control.” Annual Review of Control, Robotics, and Autonomous Systems, 2019. arXiv:1806.09460.
  • DDPG: Spinning Up documentation and the original paper, arXiv:1509.02971.

Process identification and state estimation ID

Main references

  • [Diehl_MSI] M. Diehl. Lecture Notes on Modelling and System Identification, University of Freiburg, 2025. Free PDF. Main text for the identification part: chapters 1–8 and appendix C.
  • [Schoukens_2013] J. Schoukens. System Identification, lecture notes, VUB, 2013.
  • [Soderstrom_1989] T. Söderström, P. Stoica. System Identification. Prentice Hall, 1989. Prediction error methods, experiment design, model validation.
  • [Ljung_1999] L. Ljung. System Identification: Theory for the User, 2nd ed. Prentice Hall, 1999. For consultation (not open access).

State estimation

  • [Sarkka_2023] S. Särkkä, L. Svensson. Bayesian Filtering and Smoothing, 2nd ed. Cambridge University Press, 2023. Free PDF on the author’s homepage.

Data-driven control and learning-based methods

  • [Coulson_2019] J. Coulson, J. Lygeros, F. Dörfler. “Data-enabled predictive control: in the shallows of the DeePC.” European Control Conference, 2019. Preprint on arXiv.
  • [Markovsky_2021] I. Markovsky, F. Dörfler. “Behavioral systems theory in data-driven analysis, signal processing, and control.” Annual Reviews in Control 52, 2021.
  • [Hewing_2020] L. Hewing, K. P. Wabersich, M. Menner, M. N. Zeilinger. “Learning-based model predictive control: toward safe learning in control.” Annual Review of Control, Robotics, and Autonomous Systems, 2020.
  • [Pillonetto_2022] G. Pillonetto, T. Chen, A. Chiuso, G. De Nicolao, L. Ljung. Regularized System Identification. Springer, 2022. Open access.

Time series

Software and data