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- W4320804317 abstract "This paper proposes a method of model predictive control (MPC) based on Gaussian process regression for stochastic linear systems with uncertainty such as unknown dynamics and disturbances. The unknown dynamics refer to an unknown function of the input and output signals. In the proposed method, the mean and variance of the unknown function values estimate the function using Gaussian process regression. Updating the system model with the estimated function enhances the performance of MPC. Moreover, the stochastic reachable set of the system reduces the chance constraints into second-order cone constraints, which means the MPC problem becomes a convex problem. Finally, numerical examples illustrate the effectiveness of the proposed MPC." @default.
- W4320804317 created "2023-02-15" @default.
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- W4320804317 date "2022-11-15" @default.
- W4320804317 modified "2023-09-25" @default.
- W4320804317 title "Stochastic Linear Model Predictive Control by Learning Uncertainty Using Gaussian Process Regression" @default.
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- W4320804317 doi "https://doi.org/10.5687/iscie.35.269" @default.
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