A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes

2021/01/25 by Viet-Anh Le, Le, Viet-Anh, Truong X. Nghiem +1 · 1 citation
Computer Science · Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #Gaussian Processes and Bayesian Inference #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2101.10351

openalex publication_date 2021/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper proposes a receding horizon active learning and control problem for dynamical systems in which Gaussian Processes (GPs) are utilized to model the system dynamics. The active learning objective in the optimization problem is presented by the exact conditional differential entropy of GP predictions at multiple steps ahead, which is equivalent to the log determinant of the GP posterior covariance matrix. The resulting non-convex and complex optimization problem is solved by the Sequential Convex Programming algorithm that exploits the first-order approximations of non-convex functions. Simulation results of an autonomous racing car example verify that using the proposed method can significantly improve data quality for model learning while solving time is highly promising for real-time applications.

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