Nonlinear Network-Induced Time Delay Systems With Stochastic Learning

• Published in: IEEE transactions on control systems technology Vol. 19; no. 4; pp. 843 - 851
• Main Authors: Hyun Cheol Cho, Fadali, M S
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-07-2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Bayesian methods; Communication system control; Computer science; control theory; systems; Computer simulation; Computer systems and distributed systems. User interface; Control system synthesis; Control systems; Control theory. Systems; Delay effects; Dynamic Bayesian model; Dynamical systems; Exact sciences and technology; Linear feedback control systems; networked control system (NCS); Networks; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear dynamics; Nonlinearity; Process control. Computer integrated manufacturing; reset control systems; Software; Stochastic systems; Studies; System theory; Time delay systems; ubiquitous sensor network (USN); Inversed pendulum; ubiquitous sensor network (USN); neural networks; Non linear control; Solid dynamic; Control synthesis; Dynamical system; Modeling; Reset control; Bayes network; Probability learning; Delay time; Electric motor; Dynamic model; Dc motor; Predictive control; Bayes estimation; reset control systems; Delay system; Neural network; Distributed system; Real time; Neurocontrollers; networked control system (NCS); Dynamic Bayesian model; Complex network; Distributed control; Wireless network; Feedback linearization; Sensor array
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2010.2056925

This paper presents a new control approach for nonlinear network-induced time delay systems by combining online reset control, neural networks, and dynamic Bayesian networks. We use feedback linearization to construct a nominal control for the system then use reset control and a neural network to compensate for errors due to the time delay. Finally, we obtain a stochastic model of the Networked Control System (NCS) using a Dynamic Bayesian Network (DBN) and use it to design a predictive control. We apply our control methodology to a nonlinear inverted pendulum and evaluate its performance through numerical simulations. We also test our approach with real-time experiments on a dc motor-load NCS with wireless communication implemented using a Ubiquitous Sensor Network (USN). Both the simulation and experimental results demonstrate the efficacy of our control methodology.