Stochastic data-driven model predictive control using Gaussian processes

Abstract

Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear controlsystems with constraints. Gaussian processes (GPs) present a powerful tool to identify the required plant model and quantifythe residual uncertainty of the plant-model mismatch. It is crucial to consider this uncertainty, since it may lead to worsecontrol performance and constraint violations. In this paper we propose a new method to design a GP-based NMPC algorithmfor finite horizon control problems. The method generates Monte Carlo samples of the GP offline for constraint tighteningusing back-offs. The tightened constraints then guarantee the satisfaction of chance constraints online. Advantages of our proposed approach over existing methods include fast online evaluation, consideration of closed-loop behaviour, and thepossibility to alleviate conservativeness by considering both online learning and state dependency of the uncertainty. The algorithm is verified on a challenging semi-batch bioprocess case study.

26 pages, 12 figures, 3 tables

References in corpus (5)

• Proceedings of the 29th International Conference on Machine Learning (ICML-12)
• Cautious Model Predictive Control using Gaussian Process Regression
• Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars
• Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning
• Stochastic Nonlinear Model Predictive Control with State Estimation by Incorporation of the Unscented Kalman Filter

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