Control with sensors/actuators assigned by Markov chains: transition rates partially unknown

Lu, Zibao; Guo, Ge

doi:10.1049/iet-cta.2012.0879

Cited by 10 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It would be very interesting to investigate the case in which both sensors and actuators are subject to communication limitation and random driving events. This problem was recently investigated in a latest paper of the authors , wherein the zero‐input strategy is assumed, and transmission delays at both sides are considered. It is worth mentioning that even for the problem with successive transmission delays, stability analysis and controller design are almost straightforward.…”

Section: Discussionmentioning

confidence: 99%

Event-driven actuators: to zero or to hold?

Guo

Shi

2013

Int. J. Robust. Nonlinear Control

Self Cite

View full text Add to dashboard Cite

SUMMARYThis paper investigates networked control systems where a number of actuators are involved but only a subset of them is assigned to be active at a time. The assignment of actuators is driven by a random event modeled as a Markov chain. When an actuator is not assigned access to the current control signal, either a zero or the output of a zero-order hold (ZOH) is used as the control signal. For both cases, a stability analysis and control design framework dependent on the states of the Markov chain is established for linear discrete systems. It is shown that the resulted sufficient and necessary stability condition for the zero-based system is more easily solvable than that of the ZOH-based case. Numerical examples have shown that the use of zero control may yield better performance than the ZOH strategy.

show abstract

Section: Discussionmentioning

confidence: 99%

Event-driven actuators: to zero or to hold?

Guo

Shi

2013

Int. J. Robust. Nonlinear Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, Markov chains can also be used to model other aspects of the NCS. For example, to drive activation states of subsets of actuators [13], [14], or subsets of sensors and actuators [15], [23]. In any case, in order to assure stability in the jumping system, some conditions can be enunciated in terms of LMIs [5] under the consideration of transmission delays both in system output measurements and in control signals.…”

Section: Introductionmentioning

confidence: 99%

A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System

Cuenca

Ojha²,

Salt

et al. 2015

Information Sciences

View full text Add to dashboard Cite

ElsevierCuenca Lacruz, ÁM.; Ojha, U.; Salt Llobregat, JJ.; Chow, M. (2015). AbstractIn this work, a non-uniform multi-rate control strategy is applied to a kind of Networked Control System (NCS) where a wireless path tracking control for an Unmanned Ground Vehicle (UGV) is carried out. The main aims of the proposed strategy are to face time-varying network-induced delays and to avoid packet disorder. A Markov chain-driven NCS scenario will be considered, where different network load situations, and consequently, different probability density functions for the network delay are assumed. In order to assure mean-square stability for the considered NCS, a decay-rate based sufficient condition is enunciated in terms of probabilistic Linear Matrix Inequalities (LMIs). Simulation results show better control performance, and more accurate path tracking, for the scheduled (delay-dependent) controller than for the non-scheduled one (i.e. the nominal controller when delays appear). Finally, the control strategy is validated on an experimental test-bed.

show abstract

“…Motivated by Guo et al [9][10][11], the problem of medium access constraints of the sensor and the actuator was discussed in [12], in which the access status of each sensor or actuator was governed by a stochastic variable modelled as a Bernoulli distributed white sequence. Along this line, Lu and Guo [13] investigated the assignment of the sensor and the actuator, different from [11], two event were modelled as two independent Markov chains taking matrix values in certain sets but with partially known transition probabilities.…”

Section: Introductionmentioning

confidence: 99%

Fault detection for a class of non‐linear networked control systems in the presence of Markov sensors assignment with partially known transition probabilities

Wang

Li³

et al. 2015

IET Control Theory & Applications

View full text Add to dashboard Cite

In this study, the problem of fault detection for a class of discrete-time non-linear networked control systems is investigated. An event modelled as a Markov chain taking matrix values in a certain set with partially known transition probabilities is utilised to characterise the phenomenon of the sensors assignment. A full-order mode-dependent fault detection filter is constructed and the corresponding fault detection problem is converted into an H ∞ filtering problem of a Markov jump system with partially known transition probabilities. Sufficient conditions for the existence of the fault detection filter are formulated as a linear matrix inequality-based convex optimisation problem. If the convex optimisation problem has a feasible solution, the corresponding fault detection filter parameters are determined. A numerical example with four cases of transition probability matrices is presented to show the usefulness of the developed method. IntroductionThe last two decades have witnessed the extensive investigation on the so-called networked control systems (NCSs). Different from the traditional point-to-point control systems, the sensor, controller and actuator of NCSs are located in different places and linked via a wire/wireless communication channels. Therefore, NCSs have been widely used in many areas such as automobiles, manufacturing plants, aircrafts and so on since they bring a lot of advantages, for example, low cost, simple installation and maintenance, increased system agility and reduced weight and so on [1, 2]. However, owing to the limited bandwidth or capacity of the inserted communication network, NCSs also give rise to a set of issues, such as network-induced delays (or transmission delays), packet dropouts (or data missing), quantisation effects, scheduling schemes and fading problems, which inevitably lead to poor system performance or even instability. Recently, the problems of stability analysis and filtering design for NCSs with the effect of network-induced delays and packet dropouts have been paid on much more attention than before, see the recent overviews on NCSs [3, 4] and references therein.Most of literatures on NCSs have only concerned the most basic issues of network-induced delays and packet dropouts without considering the problems of others. In fact, a scheduling scheme or an activation management policy is also an important issue for NCSs and should not be ignored, especially for a large system with numerous sensors and actuators, which cannot be simultaneously accommodated by the network with limited bandwidth/capacity or limited power supply. Therefore the concept of communication sequence first was presented in [5], which investigated the network scheduling when some NCSs were linked to the network and with the assumption that the network bandwidth is arbitrary, meanwhile, it applies the rate monotonic scheduling algorithm to schedule a set of NCSs. And what follows, Walsh et al. [2] introduced the try-once-discard (TOD) protocol for multiple-input-multiple-output NCSs, whi...

show abstract

Control with sensors/actuators assigned by Markov chains: transition rates partially unknown

Cited by 10 publications

References 24 publications

Event-driven actuators: to zero or to hold?

Event-driven actuators: to zero or to hold?

A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System

Fault detection for a class of non‐linear networked control systems in the presence of Markov sensors assignment with partially known transition probabilities

Contact Info

Product

Resources

About