Event‐triggered predictive control for networked control systems with network‐induced delays and packet dropouts

Abstract

Summary This paper presents an event‐triggered predictive control approach to stabilize a networked control system subject to network‐induced delays and packet dropouts, for which the states are not measurable. An observer‐based event generator is first designed according to the deviation between the state estimation at the current time and the one at the last trigger time. A predictive control scheme with a selector is then proposed to compensate the effect of network‐induced delays and packet dropouts. Sufficient conditions for stabilization of the networked control system are derived by solving linear matrix inequalities and the corresponding gains of the controller and the observer are obtained. It is shown that the event‐triggered implementation is able to realize reduction in communication and save bandwidth resources of feedback channel networks. A simulation example of an inverted pendulum model illustrates the efficacy of the proposed scheme.

Event‐triggered observer‐based robust H∞ control for networked control systems with unknown disturbance

“…Yet, along with these advantages, some challenging issues (bandwidth allocation, communication delay, packet dropouts, packet disorders, and channel fading) emerged and gave rise to much attention. Then most recently, the research on the NCSs has become a heated topic and many elegant results have been proposed . For instance, in Reference , an optimal linear filter for the NCSs was designed with involving time‐correlated fading channels.…”

“…Up until now, some effective approaches have been reported, such as time‐triggered strategy, event‐triggered scheme (ETS), and hybrid‐triggered one . Unlike the time‐trigger, since the ETS can be more efficient in reducing the number of transmitted data, thus recently, plenty of event‐based techniques have been proposed to study various control systems including the NCSs . In Reference , a controller design on the combination of three matrices Q, S, R (QSR)‐dissipativity was proposed by using a continuous‐time ETS, in which Zeno behaviors could be avoided.…”

“…In Reference , a controller design on the combination of three matrices Q, S, R (QSR)‐dissipativity was proposed by using a continuous‐time ETS, in which Zeno behaviors could be avoided. The combinations of event‐based scheme and predictive control were addressed, in which the nonlinear NCS was involved . Except for the event schemes concerning continuous‐time case, since an effective event technique based on sampled data was proposed, it has been widely utilized and improved .…”

“…In Reference , some linear matrix inequality (LMI) results on output feedback H ∞ control were derived, and in Reference , the dual‐rate control for wireless NCSs with applications to the unmanned aerial vehicles were discussed. Yet, it worth noting that in References , since the triggering thresholds were fixed constants and could not adjust to the variation of the NCSs, some adaptive event‐triggered schemes (AETSs) were proposed and their thresholds could be regulated in real time. In References and , two types of AETSs were, respectively, presented to study the fault detection and H ∞ output tracking control, where the thresholds could be adjusted by using the online algorithms.…”

Quantized event‐triggered H∞ control of linear networked systems with time‐varying delays and packet losses

“…The control law is designed with combining varying transmission intervals and varying delays. In other words, the minimum and maximum inter‐event times is obtained to ensure the stability and provide suitable performance; (ii) the other one is to make models with the stochastic technique in which network‐induced delays and/or packet losses process are assigned by specified probability distributions, such as Bernoulli distributed white sequences or Markov chains . Defining a stochastic process leads to explicitly modeling the network‐induced delays for real dynamic NCSs (ie, Markov process) and is consequently applied to the control design .…”