C-GLISp: Preference-Based Global Optimization Under Unknown Constraints With Applications to Controller Calibration

• Published in: IEEE transactions on control systems technology Vol. 30; no. 5; pp. 2176 - 2187
• Main Authors: Zhu, Mengjia, Piga, Dario, Bemporad, Alberto
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active preference learning; Algorithms; Benchmark testing; Calibration; Control theory; Global optimization; global optimization with unknown constraints; Linear programming; Machine learning; model predictive control (MPC); Optimization; Parameters; Preferences; Space exploration; Task analysis; Tuning

Preference-based global optimization algorithms minimize an unknown objective function only based on whether the function is better, worse, or similar for given pairs of candidate optimization vectors. Such optimization problems arise in many real-life examples, such as finding the optimal calibrati...