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Cautious Model Predictive Control Using Gaussian Process Regression
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DOI：10.1109/TCST.2019.2949757.png)
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摘要
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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.
Keyword:
Predictive control
Data models
Computational modeling
Kernel
Gaussian processes
Uncertainty
Predictive models
Autonomous racing
Gaussian processes (GPs)
learning-based control
model learning
model predictive control (MPC)
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3.9
论文数:
4.9K
被引数:
1.7W
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Explicit stochastic predictive control of combustion plants based on Gaussian process models.png)
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