Advancing Robust Control: A Comparative Study of Model-Based, Data-Driven and Learning-Enhanced Predictive Strategies

Chandar Kumar, Dur Muhammad Soomro, Najeeb Ur Rehman Malik

Abstract

The purpose of this article is to propose a novel machine learning–enhanced data-driven model predictive control (DD-ML-MPC) to improve robustness and performance under uncertainty. The article describes a DD-ML-MPC method, based on neural-network predictors trained on historical I/O data and integrated within an MPC framework, enabling more accurate forecasts and tighter constraint satisfaction when plant models are unavailable. Using numerical simulations on a MIMO LTI system corrupted by Gaussian noise, the authors compare three strategies—Model-Based MPC (MB-MPC), Data-Driven MPC (DD-MPC) and DD-ML-MPC—by assessing root mean square error, convergence time, constraint violation rate and computational overhead. The proposed DD-ML-MPC is demonstrated on a four-state, two-input, two-output plant subjected to stochastic disturbances. Our DD-ML-MPC achieves a 20 % reduction in constraint violations and a 15 % decrease in tracking error relative to MB-MPC.
The evaluation methodology is validated through Monte Carlo analysis. The evaluation criteria also include stability margins and computational efficiency under real-time constraints. New research results bridge model-based and data-driven paradigms, offering practical applications in autonomous vehicles, process control and robotics. Results demonstrate the potential for field deployment and suggest future extensions to nonlinear and time-varying systems. This paper is novel because it introduces a unified control paradigm that integrates deep learning predictors into MPC, yielding robustness and scalability improvements.

 

Keywords: Model Predictive Control (MPC); Data-Driven Control; Machine Learning Control; Neural Network.

 

https://doi.org/10.55463/issn.1674-2974.52.5.15


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References


C. E. Garcia, D. M. Prett, and M. Morari, “Model predictive control: Theory and practice—A survey,” Automatica, vol. 25, no. 3, pp. 335–348, 1989.

L. Ljung, “Perspectives on system identification,” Annual Reviews in Control, vol. 34, no. 1, pp. 1–12, 2010.

D. Q. Mayne, “Model predictive control: Recent developments and future promise,” Automatica, vol. 50, no. 12, pp. 2967–2986, 2014.

Z.-S. Hou and Z. Wang, “From model-based to data-driven control: A survey,” Information Sciences, vol. 235, pp. 3–35, 2013.

S. Levine, A. Agarwal, A. K. Tamar, and A. A. Rakhimian, “Offline reinforcement learning: Tutorial, review, and perspectives on open problems,” arXiv preprint arXiv:2005.01643, 2020.

M. Morari and J. H. Lee, “Model predictive control: Past, present, and future,” Computers & Chemical Engineering, vol. 23, nos. 4–5, pp. 667–682, 1999.

S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Engineering Practice, vol. 11, no. 7, pp. 733–764, 2003.

M. Neunert, T. Stäuble, J. Buchli, and S. Schaal, “Fast nonlinear model predictive control for unified trajectory optimization and tracking,” in Proc. IEEE Int. Conf. Robotics Automat., 2016, pp. 689–695.

X. Song et al., “A review of the motion planning and control methods for automated vehicles,” Sensors, vol. 23, no. 6140, 2023, doi:10.3390/s23136140.

M. A. Brdys and T. Chang, “Robust model predictive control under output constraints,” IFAC Proc. Volumes, vol. 35, no. 1, pp. 41–46, 2002.

J. Iqbal, M. A. Khan, S. I. Jamil, and M. N. Naeem, “Nonlinear control systems—A brief overview of historical and recent advances,” Nonlinear Engineering, vol. 6, no. 4, pp. 301–312, 2017.

J. Wang, R. A. Ramirez-Mendoza, and R. Morales-Menendez, Data Driven Strategies: Theory and Applications, CRC Press, 2023.

G. Pillonetto, F. Dinucci, G. Bisiacchi, and G. Chiuso, “Kernel methods in system identification, machine learning, and function estimation: A survey,” Automatica, vol. 50, no. 3, pp. 657–682, 2014.

J. C. Willems, P. van den Hof, P. Rapisarda, and H. L. Trentelman, “A note on persistency of excitation,” Systems & Control Letters, vol. 54, no. 4, pp. 325–329, 2005.

D. Sturzenegger, D. Gwerder, M. Guglielmetti, and M. Morari, “MPC for energy-efficient buildings: State of the art and future trends,” Journal of Process Control, vol. 74, pp. 72–87, 2019.

Y. Zhang, C. Chen, K. Zhou, and Y. Yin, “Robust model predictive control for optimal energy management of island microgrids with uncertainties,” Energy, vol. 164, pp. 1229–1241, 2018.

M. Guo, C. De Persis, and P. Tesi, “Data-driven stabilization of nonlinear polynomial systems with noisy data,” IEEE Trans. Autom. Control, vol. 67, no. 8, pp. 4210–4217, 2021.

S. J. Qin, “Data-driven fault detection and diagnosis for complex industrial processes,” IFAC Proc. Volumes, vol. 42, no. 8, pp. 1115–1125, 2009.

K. Seel, B. Ranft, and F. Allgöwer, “Neural network-based model predictive control with input-to-state stability,” in Proc. Amer. Control Conf., 2021.

Y. Cao and R. B. Gopaluni, “Deep neural network approximation of nonlinear model predictive control,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 11319–11324, 2020.

S. Gros and M. Zanon, “Reinforcement learning for adaptive MPC,” Automatica, vol. 131, Art. no. 109754, 2021.

F. Borrelli, A. Bemporad, and M. Morari, “Robust MPC for uncertain systems,” IEEE Trans. Autom. Control, vol. 68, no. 4, pp. 2103–2118, 2023.

J. Berberich, P. Köhler, and F. Allgöwer, “Robust constraint satisfaction in data-driven MPC,” in Proc. 59th IEEE Conf. Decis. Control, 2020.

E. Bradford, Y. Liu, J. Hayden, and M. Zavlanos, “Stochastic data-driven model predictive control using Gaussian processes,” Computers & Chemical Engineering, vol. 139, Art. no. 106844, 2020.

M. P. Deisenroth, D. Fox, and C. E. Rasmussen, “Gaussian processes for data-efficient learning in robotics and control,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 2, pp. 408–423, 2015.

V. Breschi, D. Piga, and A. Bemporad, “Data-driven control of multi-input systems: A subspace approach,” IEEE Trans. Control Syst. Technol., vol. 31, no. 6, pp. 2456–2468, 2023.

J. Berberich, P. Köhler, L. Coutin, and F. Allgöwer, “Data-driven tracking MPC for changing setpoints,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 6923–6930, 2020.

P. G. Carlet, D. Pucci, G. Palladini, and A. Franchi, “Data-driven continuous-set predictive current control for synchronous motor drives,” IEEE Trans. Power Electron., vol. 37, no. 6, pp. 6637–6646, 2022.

B. Houska, J. A. Ferreau, and M. Diehl, “Distributed nonlinear MPC: A survey,” Annu. Rev. Control, vol. 50, pp. 89–102, 2021.

Z. Hou and Z. Wang, “From model-based control to data-driven control: Survey, classification and perspectives,” Annu. Rev. Control, vol. 37, no. 1, pp. 47–60, 2013.

J. B. Rawlings and D. Q. Mayne, Model Predictive Control: Theory and Design, Nob Hill Publishing, Madison, WI, 2009.

C. Kumar, M. Sabih, S. Kumar, Z. Ali, and A. M. Khan, “Data-driven control design for complex systems: Theory, challenges, and novel applications,” Sukkur IBA J. Comput. Math. Sci., vol. 8, no. 1, pp. 56–73, 2024.


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