Cautious Model Predictive Control Using Gaussian Process Regression
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Date
2020-11
Publication Type
Journal Article
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yes
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Abstract
Gaussian process (GP) regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in function estimation. In the context of control, it is seeing increasing use for modeling of nonlinear dynamical systems from data, as it allows the direct assessment of residual model uncertainty. We present a model predictive control (MPC) approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a GP. We describe a principled way of formulating the chance-constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control. Using additional approximations for efficient computation, we finally demonstrate the approach in a simulation example, as well as in a hardware implementation for autonomous racing of remote-controlled race cars with fast sampling times of 20 ms, highlighting improvements with regard to both performance and safety over a nominal controller.
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published
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Journal / series
Volume
28 (6)
Pages / Article No.
1237 - 1259
Publisher
IEEE
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Edition / version
Methods
Software
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Date collected
Date created
Subject
Model Predictive Control; Gaussian Processes; Learning-based Control; Model Learning; Autonomous Racing
Organisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
Notes
Funding
157601 - Safety and Performance for Human in the Loop Control (SNF)