2023/12/06 by Rahel Rickenbach, Rickenbach, Rahel, Anna Scampicchio +3 · 2 citations
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2312.03532
openalex publication_date 2023/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Inverse optimal control (IOC) is about estimating an unknown objective of interest given its optimal control sequence. However, truly optimal demonstrations are often difficult to obtain, e.g., due to human errors or inaccurate measurements. This paper presents an IOC framework for objective estimation from multiple sub-optimal demonstrations in constrained environments. It builds upon the Karush-Kuhn-Tucker optimality conditions, and addresses the Errors-In-Variables problem that emerges from the use of sub-optimal data. The approach presented is applied to various systems in simulation, and consistency guarantees are provided for linear systems with zero mean additive noise, polytopic constraints, and objectives with quadratic features.