NDLI: Supervisory output prediction for bilinear systems by reinforcement learning

Content Provider	IET Digital Library
Author	Chasparis, Georgios C. Natschläger, Thomas
Abstract	Online output prediction is an indispensable part of any model predictive control implementation. For several application scenarios, operating conditions may change quite often, while designing the data collection process may not be an option. To this end, this study introduces a supervisory output prediction scheme, tailored specifically for input–output stable bilinear systems, that intends on automating the process of selecting the most appropriate prediction model during runtime. The selection process is based upon a reinforcement-learning scheme, where prediction models are selected according to their prior prediction performance. An additional selection process is concerned with appropriately partitioning the control-inputs' domain also to allow for switched-system approximations of the original bilinear dynamics. The authors show analytically that the proposed scheme converges (in probability) to the best model and partition. They also demonstrate these properties through simulations of temperature prediction in residential buildings.
Starting Page	1514
Ending Page	1521
Page Count	8
ISSN	17518644
Volume Number	11
e-ISSN	17518652
Issue Number	Issue 10, Jun (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/11/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2016.1400
Journal	IET Control Theory & Applications
Publisher Date	2017-03-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bilinear System Buildings (structures) Control-input Domain Data Collection Process Digital Simulation Input-output Stable Bilinear System Knowledge Engineering Technique Learning in AI Model Predictive Control Implementation Nonlinear Control System Online Output Prediction Optimal Control Original Bilinear Dynamics Predictive Control Prior Prediction Performance Reinforcement Learning Residential Buildings Supervisory Output Prediction Switched-system Approximation Switching System (control) Temperature Control Temperature Prediction Simulation Thermal Variables Control Time Varying Control System
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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