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

TL;DR

This paper introduces a stochastic model predictive control method for reference tracking over unreliable communication channels, effectively handling noise, data losses, and bounded control actions through a novel transmission protocol and disturbance feedback policies.

Contribution

It presents a new MPC framework that manages unreliable channels and unbounded noise with a tractable quadratic program and specialized control policies.

Findings

Ensures mean-square boundedness of tracking error.

Provides a computationally efficient quadratic program.

Handles data losses with dropout compensation.

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.