Enhanced active learning for model-based predictive control with safety guarantees
REN Rui
ZOU Yuan-yuan
LI Shao-yuan
Abstract：This paper proposes an active learning-based MPC scheme that overcomes the shortcomings of most learning-based methods which passively leverage the available system data and result in slow learning. We first apply Gaussian process regression to assess the residual model uncertainty and construct multi-step predictive model. Then we propose a two-step active learning strategy and reward the system probing by introducing information gain as dual objective in the optimization problem. Finally, the safe control input set is defined based on robust admissible input set to robustly guarantee state constraint satisfaction. The proposed method improves the learning ability and closed-loop performance with safety guarantees. The advantages of our proposed active learning-based MPC scheme are illustrated in the experiments.
Keywords：model predictive controlactive learningGaussian process regressiondual controlinformation gain
Publication Date：2021-11-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1735-1742 )