Consensus Tracking Control of Switched Stochastic Nonlinear Multiagent Systems via Event-Triggered Strategy

Zou, Wencheng; Shi, Peng; Xiang, Zhengrong; Shi, Yan

doi:10.1109/tnnls.2019.2917137

Cited by 273 publications

(140 citation statements)

References 35 publications

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“…Therefore, we generally choose a relative large values of C in the algorithm corresponding to assigning a higher penalty to errors [31]. 3 , where n sv is the number of support vectors at each iteration, s is the number of submodels. Therefore, the total computational complexity of modified self-training SVM is…”

Section: Algorithm 1 Cluster-based Algorithm Inputmentioning

confidence: 99%

A Semi-Supervised Learning Approach for Identification of Piecewise Affine Systems

Liu

Qiu

et al. 2020

IEEE Trans. Circuits Syst. I

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Piecewise affine (PWA) models are attractive frameworks that can represent various hybrid systems with local affine submodels and polyhedral regions due to their universal approximation properties. The PWA identification problem amounts to estimating both the submodel parameters and the polyhedral partitions from data. In this paper, we propose a novel approach to address the identification problem of PWA systems such that the number of submodels, parameters of submodels, and the polyhedral partitions are obtained. In particular, a cluster-based algorithm is designed to acquire the number of submodels, the initial labeled data set, and initial parameters corresponding to each submodel. Additionally, we develop a modified self-training support vector machine algorithm to simultaneously identify the hyperplanes and parameter of each submodel with the ouputs of the cluster-based algorithm. The proposed algorithm is computationally efficient for region estimation and able to accomplish this task with only a small quantity of classified regression vectors. The effectiveness of the proposed identification approach is illustrated via simulation results.

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Section: Algorithm 1 Cluster-based Algorithm Inputmentioning

confidence: 99%

A Semi-Supervised Learning Approach for Identification of Piecewise Affine Systems

Liu

Qiu

et al. 2020

IEEE Trans. Circuits Syst. I

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“…However, in practical applications, the agents are usually equipped with digital microprocessor, resulting in limited energy storage and communication resources. In order to avoid excessive information transmission and reduce the consumption of communication resources, an effective method is to use event‐triggered strategies to determine the updates of the controller 15‐34 . Dimarogonas et al 16 presented centralized and distributed event‐triggered strategies to ensure consensus of first‐order model and generalize the results to self‐triggered case.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered mean square consensus of linear multiagent systems with measurement noises

2020

Adaptive Control & Signal

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Summary This article investigates mean square consensus of linear multiagent systems with measurement noises based on event‐triggered control. Considering various uncertain factors in the communication environment, multiplicative noises exists in the information that each agent measures from its neighbors. In order to save limited resources and to exclude the Zeno phenomenon, the update of the controller for each agent is driven by properly defined event‐triggered condition. Sufficient conditions are given to ensure exponential mean square consensus of multiagent systems with multiplicative noises by utilizing static and dynamic event‐triggered scheme. The interevent times of static event‐triggered mechanism is less than those of corresponding dynamic case for each agent. Finally, validity of the theoretical results is illustrated through a simulation example.

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“…Moreover, Cao et al studied the consensus tracking of nonlinear agents with hybrid varying directed topologies in [14]. Using an event-triggered strategy, Zou et al provided the consensus tracking protocol of nonlinear multi-agent systems with switched stochastic networks in [15]. Yoo gave a a universal control strategy to solve asynchronously switched nonlinear multi-agent systems for the consensus tracking in [16].…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Consensus Tracking of Nonlinear Heterogeneous Multi-Agent Systems With Disturbances and Directed Graphs

Pei

2020

IEEE Access

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This paper focuses on consensus tracking problem of heterogeneous multi-agent systems (HMASs) with disturbances and directed graphs. First, nonlinear dynamical models are given for first-order, second-order agents and mobile target. With introducing a distributed filtering method, the immeasurable velocities of agents and the mobile target are estimated by their positional informations. The proposed integral sliding mode control protocols can implement on finite-time consensus tracking of HMASs. Then, using Lyapunov method, the stability of heterogeneous system is proved, and the sufficient condition is provided for achieving the finite-time consensus tracking. The simulation example further illustrates the effectiveness of theoretical results. INDEX TERMS Heterogeneous multi-agent systems, nonlinear dynamics, consensus tracking, sliding mode control.

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Consensus Tracking Control of Switched Stochastic Nonlinear Multiagent Systems via Event-Triggered Strategy

Cited by 273 publications

References 35 publications

A Semi-Supervised Learning Approach for Identification of Piecewise Affine Systems

A Semi-Supervised Learning Approach for Identification of Piecewise Affine Systems

Event‐triggered mean square consensus of linear multiagent systems with measurement noises

Finite-Time Consensus Tracking of Nonlinear Heterogeneous Multi-Agent Systems With Disturbances and Directed Graphs

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