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- W2409968204 abstract "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." @default.
- W2409968204 created "2016-06-24" @default.
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- W2409968204 date "2015-01-01" @default.
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- W2409968204 title "Risk-Sensitive Model Predictive Control with Gaussian Process Models**This research was supported by EU Framework Programme 7, project 314544, RECONFIGURE: Reconfiguration of Control in Flight for Integral Global Upset Recovery, the Cambridge Overseas Trust, and by the China Scholarship Council." @default.
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- W2409968204 doi "https://doi.org/10.1016/j.ifacol.2015.12.156" @default.
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