Event-triggered control for multi-agent systems with output saturation

Yi, Xinlei; Yang, Tao; Wu, Junfeng; Johansson, Karl H.

doi:10.23919/chicc.2017.8028693

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…A fuzzy adaptive two‐bits‐triggered control method is proposed to guarantee the stability of nonlinear uncertain systems with input saturation and output constraint by Chen et al 23 The actuator saturation and matched uncertainties problems of discrete‐time singular systems are investigated by Jafari and Binazadeh, 24 where an additional control term is added into the composite nonlinear feedback controller to enhance the robustness of the designed output regulation algorithm. Moreover, the output saturation problems for MASs to perform scaled consensus tasks 25 or event‐triggered consensus tasks 26 are also studied.…”

Section: Introductionmentioning

confidence: 99%

“…This model is suitable for heterogeneous and time‐varying parameters MASs. Compared with the existing MBC approaches, 1‐26 the model structure is simpler and computation burdens can be significantly reduced. (ii) Only the input and saturated output data are utilized to design the DMFACTC approach, which can reduce each agent's tracking errors to guarantee the tracking performance of MASs under fixed and switching topologies.…”

Section: Introductionmentioning

confidence: 99%

“…(ii) Only the input and saturated output data are utilized to design the DMFACTC approach, which can reduce each agent's tracking errors to guarantee the tracking performance of MASs under fixed and switching topologies. Although several effective algorithms for the output saturation problems are considered in the existing works, [22][23][24][25][26] they are dependent on accurate dynamics, which is not easy to be established. (iii) The proposed DMFACTC algorithm is formulated by utilizing input and saturated output information, where the output data also includes measurement disturbance.…”

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confidence: 99%

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Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Zhao

2021

Intl J Robust & Nonlinear

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This article proposes a distributed model‐free adaptive consensus tracking control (DMFACTC) approach for a class of unknown heterogeneous nonlinear discrete‐time multi‐agent systems (MASs) with sensor saturation and measurement disturbance to perform consensus tracking tasks. Meanwhile, both fixed and switching topologies are considered, where only a subset of agents can acquire the desired trajectory information in each topology. A time‐varying linear data model for each agent is first established by utilizing the dynamic linearization method to formulate this algorithm. Merely, the input data and the saturated output data with measurement disturbance of each agent are applied to construct the DMFACTC algorithm without employing any dynamics model information of MASs. The convergence of the designed scheme is strictly proved. It illustrates that the output saturation and switching topologies do not affect the stability of MASs. Moreover, even if the sensor saturation, measurement disturbance, and switching topologies happen simultaneously, the DMFACTC also guarantees that the tracking errors of MASs converge to a small range around the origin. Furthermore, two numerical simulations and a realistic filling system simulation further verify the correctness and effectiveness of the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Zhao

2021

Intl J Robust & Nonlinear

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“…However, the stable control of cooperative UAVs is transformed into the stable control of the agent system. Many interesting applications of multi-UAVs flight include dealing with the consistency problems, [3][4][5][6][7] applications in clusters 8 and radar deception. 9 Main considerations in the formation control of a multi-agent system include how to achieve flight stability.…”

Section: Introductionmentioning

confidence: 99%

Design and flight-stability analysis of a closed fixed-wing unmanned aerial vehicle formation controller

Zhang

Xiao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article addresses a flight-stability problem for the multiple unmanned aerial vehicles cooperative formation flight in the process of the closed and high-speed flight. The main objective is to design a cooperative formation controller with known external factors, and this controller can keep the consensus of attitude and position and reduce the communication delay between any two unmanned aerial vehicles and increase unmanned aerial vehicles formation cruise time under the known external factors. Known external factors are taken into consideration, and longitude maneuvers using nonlinear thrust vectors were employed with unsteady aerodynamic models, according to the attitude and position of unmanned aerial vehicles, which were employed as corresponding input signals for studying the dynamic characteristics of unmanned aerial vehicles formation flight. In addition, the relative distance between any two unmanned aerial vehicles was not allowed to exceed their safe distance so that the controller could perform collision avoidance. An analysis of formation flight distance error shows that it converged to a fixed value that well ensured unmanned aerial vehicles formation flight stability. The experimental results show that the controller can improve the speed of a closed formation effectively and maintain the stability of formation flight, which provides a method for closed formation flight controller design and collision avoidance for any two unmanned aerial vehicles. Meanwhile, the effectiveness of proposed controller is fully proved by semi-physical simulation platform.

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“…The event triggered SMC was applied to the nonlinear system in [26], which is affected by the external disturbance. To solve the consensus problem for multiagent systems with output saturation, the distributed static and dynamic event triggered control law was proposed in [27]. Usually, the triggering event is decided by the discrete error [28], which is hard to realization and result the Zeno phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Speed Regulation of Permanent Magnet Synchronous Motor Using Event Triggered Sliding Mode Control

Zhang

Liu

Zhang

et al. 2018

Mathematical Problems in Engineering

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To reduce the chattering issue and improve the dynamic tracking performance of the speed regulation of the permanent magnet synchronous motor (PMSM) system, the sliding mode control (SMC) with an enhanced exponential reaching law (EERL) is proposed in this paper. Compared to the SMC with conventional reaching law (CRL), the proposed SMC based on the EERL makes the convergence rate be associated with the change of the system state variable. In order to implement the minimum resource utilization of the embedded processor while guaranteeing the performance of the system, the event triggered strategy is developed for SMC, which possesses the excellent robustness and saves the energy cost. The stability of the event triggered SMC control law is proved by the Lyapunov function and the minimum control execution time interval is also derived. Finally, the effectiveness of the proposed EERL and the satisfactory performance of the event triggered SMC are demonstrated by the simulation results.

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Event-triggered control for multi-agent systems with output saturation

Cited by 7 publications

References 24 publications

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Design and flight-stability analysis of a closed fixed-wing unmanned aerial vehicle formation controller

Speed Regulation of Permanent Magnet Synchronous Motor Using Event Triggered Sliding Mode Control

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