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arXiv · 2006.04367

Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

Prabhat K. Mishra·Sanket S. Diwale·Colin N. Jones·Debasish Chatterjee
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Abstract

A stochastic model predictive control framework over unreliable Bernoulli communication channels, in the presence of unbounded process noise and under bounded control inputs, is presented for tracking a reference signal. The data losses in the control channel are compensated by a carefully designed transmission protocol, and that of the sensor channel by a dropout compensator. A class of saturated, disturbance feedback policies is proposed for control in the presence of noisy dropout compensation. A reference governor is employed to generate trackable reference trajectories and stability constraints are employed to ensure mean-square boundedness of the reference tracking error. The overall approach yields a computationally tractable quadratic program, which can be iteratively solved online.

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Prabhat K. Mishra, Sanket S. Diwale, Colin N. Jones, Debasish Chatterjee. 2020-12-24. Reference tracking stochastic model predictive control over unreliable channels and bounded control actions. https://doi.org/10.1016/j.automatica.2021.109512

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