Reference tracking stochastic model predictive control over unreliable channels and bounded control actions
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.
Cite
@article{arxiv.2006.04367,
title = {Reference tracking stochastic model predictive control over unreliable channels and bounded control actions},
author = {Prabhat K. Mishra and Sanket S. Diwale and Colin N. Jones and Debasish Chatterjee},
journal= {arXiv preprint arXiv:2006.04367},
year = {2020}
}