Learning and Control using Gaussian Processes

Thumbnail Image
Penn collection
Real-Time and Embedded Systems Lab (mLAB)
Degree type
CPS Efficient Buildings
CPS Real-Time
CPS Theory
Machine learning
Gaussian Processes
optimal experiment design
receding horizon control
active learning
Computer Engineering
Controls and Control Theory
Design of Experiments and Sample Surveys
Dynamical Systems
Electrical and Computer Engineering
Grant number
Copyright date
Related resources
Nghiem, Truong X
Morari, Manfred
Mangharam, Rahul

Building physics-based models of complex physical systems like buildings and chemical plants is extremely cost and time prohibitive for applications such as real-time optimal control, production planning and supply chain logistics. Machine learning algorithms can reduce this cost and time complexity, and are, consequently, more scalable for large-scale physical systems. However, there are many practical challenges that must be addressed before employing machine learning for closed-loop control. This paper proposes the use of Gaussian Processes (GP) for learning control-oriented models: (1) We develop methods for the optimal experiment design (OED) of functional tests to learn models of a physical system, subject to stringent operational constraints and limited availability of the system. Using a Bayesian approach with GP, our methods seek to select the most informative data for optimally updating an existing model. (2) We also show that black-box GP models can be used for receding horizon optimal control with probabilistic guarantees on constraint satisfaction through chance constraints. (3) We further propose an online method for continuously improving the GP model in closed-loop with a real-time controller. Our methods are demonstrated and validated in a case study of building energy control and Demand Response.

Date of presentation
Conference name
Real-Time and Embedded Systems Lab (mLAB)
Conference dates
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher DOI
Journal Issue
Recommended citation