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Risk-Sensitive Model Predictive Control with Gaussian Process Models

Yang, X and Maciejowski, J (2015) Risk-Sensitive Model Predictive Control with Gaussian Process Models. IFAC-PapersOnLine, 48. pp. 374-379.

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This paper proposes the use of risk-sensitive costs in a model predictive controller (MPC) with Gaussian process (GP) models, for more effective online learning and control. Being a probabilistic model, a GP incorporates the uncertainty information due to imperfect knowledge of the system. The MPC then utilises this uncertainty information in a risk-sensitive, especially risk-seeking fashion, to balance the exploration of the unknown characteristics and the exploitative control actions simultaneously. Comparison of MPCs with the risk-seeking cost and the risk-neutral cost, i.e. the standard quadratic cost, on the swing-up control of a cart-pendulum system demonstrates that the risk-seeking cost exhibits an effective exploratory behaviour which leads to a better learning of the unknown system and in turn gives improved control performance.

Item Type: Article
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:31
Last Modified: 09 Sep 2021 01:02
DOI: 10.1016/j.ifacol.2015.12.156