Open AccessResource efficient stochastic predictive control under packet dropouts
Author(s) -
Mishra Prabhat K.,
Chatterjee Debasish,
Quevedo Daniel E.
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0879
Subject(s) - control theory (sociology) , neighbourhood (mathematics) , computer science , model predictive control , controller (irrigation) , network packet , bounded function , stochastic control , mathematical optimization , control (management) , optimal control , mathematics , artificial intelligence , computer network , mathematical analysis , agronomy , biology
This study presents a resource efficient framework for a class of stochastic control systems that utilises state dependent control strategies in order to reduce the online computational load. When the states are in a given neighbourhood of the desired operating point, the controller is switched off, and data from the feedback channel is not transmitted. Outside this neighbourhood, the authors pay close attention to the performance of the controller by adopting a stochastic predictive algorithm when the states are in a predefined comfort zone, and activate a recovery algorithm beyond the comfort zone that secures at least good qualitative properties. The authors demonstrate that the proposed controller leads to mean square boundedness of the closed loop states in the presence of stochastic noise, bounded control authority, and control channel erasures, while entailing a dramatic reduction in network traffic and computational resources.