Harnessing Data for Accelerating Model Predictive Control by Constraint Removal
Abstract: Model predictive control (MPC) solves a receding-horizon optimization problem in real-time, which can be computationally demanding when there are thousands of constraints. To accelerate online computation of MPC, we utilize data to adaptively remove the constraints while maintaining the MPC policy unchanged. Specifically, we design the removal rule based on the Lipschitz continuity of the MPC policy. This removal rule can use the information of historical data according to the Lipschitz constant and the distance between the current state and historical states. In particular, we provide the explicit expression for calculating the Lipschitz constant by the model parameters. Finally, simulations are performed to validate the effectiveness of the proposed method.
- G. Cesari, G. Schildbach, A. Carvalho, and F. Borrelli, “Scenario model predictive control for lane change assistance and autonomous driving on highways,” IEEE Intelligent transportation systems magazine, vol. 9, no. 3, pp. 23–35, 2017.
- 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.
- C. M. Best, M. T. Gillespie, P. Hyatt, L. Rupert, V. Sherrod, and M. D. Killpack, “A new soft robot control method: Using model predictive control for a pneumatically actuated humanoid,” IEEE Robotics & Automation Magazine, vol. 23, no. 3, pp. 75–84, 2016.
- J. G. Van Antwerp and R. D. Braatz, “Model predictive control of large scale processes,” Journal of Process Control, vol. 10, no. 1, pp. 1–8, 2000.
- S. Hovland, K. Willcox, and J. T. Gravdahl, “Mpc for large-scale systems via model reduction and multiparametric quadratic programming,” in Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006, pp. 3418–3423.
- A. Bemporad, M. Morari, V. Dua, and E. N. Pistikopoulos, “The explicit linear quadratic regulator for constrained systems,” Automatica, vol. 38, no. 1, pp. 3–20, 2002.
- J. L. Jerez, E. C. Kerrigan, and G. A. Constantinides, “A condensed and sparse qp formulation for predictive control,” in 2011 50th IEEE Conference on Decision and Control and European Control Conference. IEEE, 2011, pp. 5217–5222.
- A. J. Ardakani and F. Bouffard, “Acceleration of umbrella constraint discovery in generation scheduling problems,” IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 2100–2109, 2014.
- S. Nouwens, B. de Jager, M. M. Paulides, and W. M. H. Heemels, “Constraint removal for mpc with performance preservation and a hyperthermia cancer treatment case study,” in 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021, pp. 4103–4108.
- M. Jost and M. Mönnigmann, “Accelerating model predictive control by online constraint removal,” in 52nd IEEE Conference on Decision and Control. IEEE, 2013, pp. 5764–5769.
- M. Jost, G. Pannocchia, and M. Mönnigmann, “Online constraint removal: Accelerating mpc with a lyapunov function,” Automatica, vol. 57, pp. 164–169, 2015.
- S. A. N. Nouwens, M. M. Paulides, and M. Heemels, “Constraint-adaptive mpc for linear systems: A system-theoretic framework for speeding up mpc through online constraint removal,” Automatica, vol. 157, p. 111243, 2023.
- P. O. Scokaert, J. B. Rawlings, and E. S. Meadows, “Discrete-time stability with perturbations: Application to model predictive control,” Automatica, vol. 33, no. 3, pp. 463–470, 1997.
- F. Fabiani and P. J. Goulart, “Reliably-stabilizing piecewise-affine neural network controllers,” IEEE Transactions on Automatic Control, vol. 68, no. 9, pp. 5201–5215, 2023.
- M. S. Darup, M. Jost, G. Pannocchia, and M. Mönnigmann, “On the maximal controller gain in linear mpc,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 9218–9223, 2017.
- D. Teichrib and M. S. Darup, “Efficient computation of lipschitz constants for mpc with symmetries,” in 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023, pp. 6685–6691.
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