Event-triggered leader-following consensus for multi-agent systems with semi-Markov switching topologies

Summary This paper investigates the consensus issue of multiagent systems with data transmission time delay. The state measurement of each local agent is directly sent to a private event‐trigger and further authorized to be broadcasted to its neighbors via communication network only when the threshold of the event‐trigger is violated. Since the controller always receives discrete‐time neighbor information with data transmission time delay, a predictor is employed to estimate the continuous‐time neighbor state. Based on the estimated state, a novel consensus protocol is mainly proposed for achieving the bounded consensus of the multiagent systems. By the proposed method, the asynchronous neighbor information is allowed and the margin of data transmission time delay is also given. Furthermore, it has been proved that the unwanted Zeno phenomena can be naturally excluded. Numerical example is provided to demonstrate the effectiveness of the proposed method.

“…However, the time‐synchronized neighbor measurement information

x_{j} false(t_{k_{i}}^{i} false)

is usually difficult to be acquired in practice. For further improving the control law, some asynchronous consensus laws were proposed in other works where the proposed consensus laws are given as

u_{i} false(t false) = K \sum_{j {\in 𝒩}_{i}} a_{i j} false(x_{i} false(t_{k_{i}}^{i} false) - x_{j} false(t_{k_{j}}^{j} false) false)

. However, because the discrete‐time measurements

x_{i} false(t_{k_{i}}^{i} false)

and

x_{j} false(t_{k_{j}}^{j} false)

are out‐of‐date with respect to the current time, the proposed control laws are time‐delayed schemes.…”

Section: Consensus Under Event‐trigger and Data Transmission Delaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bounded consensus for multiagent systems by event‐triggered data transmission, time delay, and predictor‐based control

Cui

2019

“…Various results about semi‐Markov jump systems have been obtained under different situations . Hou et al introduced finite phase semi‐Markov processes.…”

Section: Introductionmentioning

confidence: 99%

“…By introducing variables and a simple transformation, every phase semi‐Markovian switching system may be equivalently expressed as its associated Markovian switching system. In most existing results, authors use the TRs and a priori information of the upper and lower bounds of the probability density function to solve problems, with the assumption that the distribution function of the sojourn time S ( n ) is

F_{i} false(t false) = double-struckP false{S false(n false) \leq t false| r_{n - 1} = i false}

, which is only dependent on the current system mode i . In the works of Zhang et al and Wu et al, the general case that the distribution function of the sojourn time

F_{i j} false(t false) = double-struckP false{S false(n false) \leq t false| r_{n} = j, r_{n - 1} = i false}

is considered, which is dependent on both the current system mode i and the target mode j to which the system jumps from the current mode.…”

Section: Introductionmentioning

confidence: 99%

Containment control of stochastic multiagent systems with semi‐Markovian switching topologies

2019

Summary The containment control of stochastic multiagent systems with semi‐Markov switching topologies is investigated in this paper. The general case that the distribution function of the sojourn time is dependent on both the current system mode and the target mode is considered. Taking state multiplicative noise into account and using stochastic techniques, sufficient conditions to achieve the containment control in the asymptotic mean square sense are obtained in a form of linear matrix inequalities and the controller design condition is given. Finally, a simulation is given to demonstrate the effectiveness of the obtained theoretical results.

“…Robust control theory and stochastic system theory are employed in stochastic stability analysis for the closed‐loop system. Besides, the method in the work of Dai and Guo, where transmission delay is ignored, cannot work here; therefore, by Dynkin's formula and Gronwall‐Bellman lemma, difficulty caused by transmission delay can be solved. 3.Under the improved DETS, in light of free‐weighting matrix method and multiple mode‐dependent Lyapunov‐Krasovskii functional candidates, less conservative results are obtained to guarantee the stabilization of a closed‐loop semi‐Markovian jump time‐delay uncertain system with a prescribed performance index.…”

Section: Introductionmentioning

confidence: 99%

H_∞ stabilization for networked semi‐Markovian jump systems with randomly occurring uncertainties via improved dynamic event‐triggered scheme

2019

Summary This paper aims to solve the H∞ stabilization problem for networked semi‐Markovian jump systems subject to randomly occurring uncertainties by an improved event‐triggered technique. A new measurement error that is defined as the difference value between the latest transmitted data and the mean value of both current data and latest transmitted data is introduced into the event‐triggered condition. Compared with traditional dynamic event‐triggered scheme, more unexpected data could be avoided to be transmitted, which is demonstrated in the simulation through sufficient comparison experiments. Furthermore, by employing a Lyapunov‐Krasovskii functional method and a free‐weighting matrix method, sufficient conditions are derived to guarantee the stabilization of the closed‐loop semi‐Markovian jump time‐delay system with uncertainties and a prescribed performance index. Then, a codesign method for H∞ controller gains and event‐triggered parameters is presented. Finally, simulations are given to verify the effectiveness of our improved dynamic event‐triggered scheme.