Lyapunov-Based Predictive Control Methodologies for Networked Control Systems

Lyapunov-Based Predictive Control Methodologies for Networked Control Systems

Constantin-Florin Caruntu (Gheorghe Asachi Technical University of Iasi, Romania)
Copyright: © 2018 |Pages: 31
DOI: 10.4018/978-1-5225-3531-7.ch005
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Abstract

The problem considered in this chapter is to control a vehicle drivetrain in order to minimize its oscillations while coping with the time-varying delays introduced by the CAN communication network and the strict timing limitations. As such, two Lyapunov-based model predictive control design methodologies are presented: one based on modeling the network-induced time-varying delays using a polytopic approximation technique and the second one based on modeling the delays as disturbances. Several tests performed using an industry validated drivetrain model indicate that the proposed design methodologies can handle both the performance/physical constraints and the strict limitations on the computational complexity, while effectively coping with the time-varying delays. Moreover, a comparative analysis between the two Lyapunov-based model predictive control design methodologies in terms of computational complexity, number of optimization variables, and obtained performances is carried out.
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Background

The evolution of standalone control systems to networked control systems brought many attractive advantages, but the use of communication networks makes it necessaryto deal with the effects of the network-induced imperfections and constraints. These are categorised into fivetypes in (Heemels et al., 2010), but, usually, the available literature considers only some of them in the analysis of NCS: (1) quantisation errors in the signals transmitted over the network; (2) packet dropouts caused by the unreliability of the network (Cloosterman et al., 2010; Munoz de la Pena and Christofides, 2008); (3) variable sampling/transmission intervals (Cloosterman et al., 2010); (4) time-varying network-induced delays, which can be smaller than the sampling period (Huang et al., 2010) or larger than the sampling period (Li et al., 2008; Cloosterman et al., 2009; Cloosterman et al., 2010); and (5) communication constraints caused by the sharing of the network (Liu et al., 2010). Moreover, an analysis of different aspects of the limitations imposed by the use of communication channels to connect the elements of NCSs is given in (Hespanha et al., 2007).

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