Work

Heuristic Automatic Scenario Elimination in Stochastic Model Predictive Control

Year: 2025

Type: article

Abstract: The performance of Model Predictive Control (MPC) degrades in the presence of uncertainties such as measurement noise, model inaccuracies, and external disturbances. To deal with this issue, stochastic MPC incorporates uncertainty into predictions, either explicitly via probability density functions or implicitly via scenarios, optimizing control actions with probabilistic constraint satisfaction guarantees. However, scenario-based approaches rely on careful scenario selection to avoid a conservative control law. To mitigate this issue, this paper uses slack variables to manage constraint violations and relieve conservatism, allowing the controller to occasionally violate constraints according to a tuning parameter that balances the trade-off between performance and risk.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.09.521

Authors: Andrés Hernández-Rivera, José M. Maestre, Takeshi Hatanaka

Institutions: Universidad de Sevilla, Institute of Science Tokyo

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación

Awards: G2106362925, G294895050