Event‐triggered output consensus for heterogeneous multiagent systems with fixed and switching directed topologies

Liu, Xiangdong; Liu, Haikuo; Du, Changkun; Lu, Pingli; Gao, Weinan

doi:10.1002/rnc.4647

Cited by 14 publications

(2 citation statements)

References 32 publications

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“…By employing the condition stated in (36) along with (35) and (31), it is possible to determine the appropriate setting for the learning rate based on the criteria outlined in (31). Consequently, this completes the proof.…”

Section: Dynamic Data-equivalent Model: Ddemmentioning

confidence: 74%

“…For a specific class of unknown discrete-time systems, data-driven control (DDC) has been introduced to address time-varying scenarios, although it has limitations when applied to linear systems with respect to the control effort. 30,31 Given the complexities outlined earlier, this work tackles the challenge of controlling practical discrete-time systems characterized by unknown mathematical models, disturbances, uncertainties, and the additional difficulty posed by time-varying behavior in the controlled plant. To address this multifaceted problem, we propose the development of a dynamic data-equivalent model (DDEM) that leverages a multi-input fuzzy rules emulated network (MiFREN) to approximate the controlled plant as an affine-like model concerning the control effort.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

Treesatayapun

2024

Intl J Robust & Nonlinear

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This article presents an adaptive controller tailored for discrete‐time systems, emphasizing robustness and addressing time‐varying constraints. An equivalent model is constructed solely from input‐output data collected from an unknown plant. Performance optimization involves determining an appropriate learning rate guided by theoretical analysis. A control law is derived from the equivalent model and a proposed auxiliary controller, with parameter settings guided by theoretical analysis to achieve robust convergence in closed‐loop performance. Comprehensive experimental and comparative assessments confirm the superior performance of both the equivalent model and the proposed controller, especially in brushless DC motor position control. Moreover, compared to similar schemes, the proposed controller generates fewer high‐frequency control effort components. The investigation validates the approach's robustness in real‐world conditions, including uncertainties, disturbances, and common time‐varying constraints in practical plant scenarios.

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Section: Dynamic Data-equivalent Model: Ddemmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

Treesatayapun

2024

Intl J Robust & Nonlinear

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Event‐triggered adaptive sliding mode control for consensus of heterogeneous nonlinear multiagent systems

Atrianfar

Yadollahi

2021

Intl J Robust & Nonlinear

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In this article, we peruse the consensus problem of nonlinear heterogeneous multiagent systems (MASs) in the presence of uncertainty and external disturbances. A new event-triggered adaptive sliding mode control is first proposed to achieve consensus for MASs consisting of heterogeneous nonlinear agents with nonidentical inherent dynamics and different-order dynamics. It is supposed that the uncertainties and their derivatives are bounded, but the upper-bounds are unknown. For reaching consensus in heterogeneous MAS, the proposed controller integrates two fundamental actions concurrently. First, an adaptive sliding-mode control strategy is adopted to guarantee the robust finite-time regulation of each individual nonlinear, uncertain, and heterogeneous agent dynamics to the sliding surface and keeping on it despite the unknown model uncertainties and disturbances. Then, the decoupled heterogeneous sliding surfaces are designed to align the agent trajectories to a consensus through local interactions, when they reach the sliding surface. Also, by applying a novel event-based strategy, we facilitate the usage of limited resources of embedded microprocessors that actuate the controller updates. We propose centralized and distributed event-triggered schemes, in which each agent decides itself when to transmit its current state to its neighbors based on the global network information or only the knowledge of their own neighbors' states. Moreover, it is shown that the interevent intervals are lower-bounded by a positive constant, and Zeno behavior is excluded. In the end, by doing simulation, verification of the theoretical results are presented.

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Distributed Dynamic Event-Triggered Output Tracking for Heterogeneous Multi-agent Systems with An Input-Bounded Leader

Liu

Bian

et al. 2022

Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021)

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Event‐triggered output consensus for heterogeneous multiagent systems with fixed and switching directed topologies

Cited by 14 publications

References 32 publications

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

Event‐triggered adaptive sliding mode control for consensus of heterogeneous nonlinear multiagent systems

Distributed Dynamic Event-Triggered Output Tracking for Heterogeneous Multi-agent Systems with An Input-Bounded Leader

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