.png){kind=icon}
.png){kind=icon}
Return
Robust learning-based MPC for nonlinear constrained systems
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2020.108948.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input-output data are available. The proposed controller is obtained using a nonparametric machine learning technique to estimate a prediction model. Based on this prediction model, a novel stabilizing robust predictive controller without terminal constraint is proposed. The design procedure is purely based on data and avoids the estimation of any robust invariant set, which is in general a hard task. The resulting controller has been validated in a simulated case study. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Predictive control
Learning control
Robust stability
Nonlinear systems
Lyapunov stability
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W