Reference tracking stochastic model predictive control over unreliable channels and bounded control actions. (May 2021)

• Record Type: Journal Article
• Authors: Mishra, Prabhat K.; Diwale, Sanket S.; Jones, Colin N.; Chatterjee, Debasish
• Is Part Of: Automatica. Volume 127(2021)
• Publication Date: 2021-05
• Subjects: Tracking -- Stochastic MPC -- Bounded controls -- Packet dropouts -- Networked system; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2021.109512
• Languages: English
• ISSNs: 0005-1098

Abstract

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 those 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.