This paper deals with the predefined-time synchronization for a class of nonlinear multi-agent systems. The notion of passivity is exploited to design the controller for predefined-time synchronization of a nonlinear multi-agent system, where the time of synchronization can be preassigned. Developed control can be used to synchronize large-scale, higher-order multi-agent systems as passivity is an important property in designing control for complex control systems, where the control inputs and outputs are considered in determining the stability of the system in contrast to other approaches, such as state-based Control We introduced the notion of predefined-time passivity and as an application of the exposed stability analysis, static and adaptive predefined-time control algorithms are designed to study the average consensus problem for nonlinear leaderless multiagent systems in predefined-time. We provide a detailed mathematical analysis of the proposed protocol, including convergence proof and stability analysis. We discussed the tracking problem for a single agent, and designed state feedback and adaptive state feedback control scheme to make tracking error predefined-time passive and then showed that in the absence of external input, tracking error reduces to zero in predefined-time. Furthermore, we extended this concept for a nonlinear multi-agent system and designed state feedback and adaptive state feedback control scheme which ensure synchronization of all the agents in predefined-time. To further strengthen the idea, we applied our control scheme to a nonlinear multi-agent system by taking the example of Chua's circuit. Finally, we compared the result of our developed predefined-time synchronization framework with finite-time synchronization scheme available in literature for the Kuramoto model.
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http://dx.doi.org/10.3390/s23083865 | DOI Listing |
Sensors (Basel)
December 2024
School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510006, China.
This paper proposes the fixed-time prescribed performance optimal consensus control method for stochastic nonlinear multi-agent systems with sensor faults. The consensus error converges to the prescribed performance bounds in fixed-time by an improved performance function and coordinate transformation. Due to the unknown faults in sensors, the system states cannot be gained correctly; therefore, an adaptive compensation strategy is constructed based on the approximation capabilities of neural networks to solve the negative impact of sensor failures.
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December 2024
College of Information Science and Engineering, and the National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, China. Electronic address:
This study constructs virtual vector triangles in multidimensional space to address cooperative control issue in time-varying nonlinear multi-agent systems. The distributed adaptive virtual point and its dynamic equations are designed, with this virtual point, the leader, and the follower being respectively defined as the vertices of the virtual vector triangle. The virtual vector edges, decomposed by vectors into coordinate axis components, are organized to form a closed virtual vector triangle by connecting the three vertices with directed vector arrows that are oriented from the tail to the head.
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December 2024
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China; Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu, 611731, Sichuan, China. Electronic address:
Neural networks have significant advantages in the estimation of uncertainty dynamics, which can afford highly accurate prediction outcomes and enhance control robustness. With this in mind, this study presents a neural network-based method to investigate the uncertain target enclosing control problem for multi-agent systems over signed networks. Firstly, a nominal target enclosing controller is constructed by adding the target information component into the classical bipartite consensus error, in which the multi-agent system can be grouped to enclose the target from opposite sides.
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December 2024
The Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, People's Republic of China. Electronic address:
In this paper, we consider the finite-time consensus problem for second-order multi-agent systems with pinning control. Unlike the existing finite-time consensus algorithms for second-order multi-agent systems in which all agents' velocities and positions are assumed to have common communication weights and nonlinear couplings, we allow communication weights, nonlinear couplings and the feedback gains to be inconsistent for each agent's velocity and position. A flexible continuous protocol is designed to solve the finite-time consensus problem.
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November 2024
Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Interdisciplinary Research Center for Sustainable Energy Systems, KFUPM, Dhahran 31261, Saudi Arabia. Electronic address:
This paper addresses the dynamic neural networks (DNNs) based resilient leader-following consensus control of multi-agent systems (MASs) under unidentified false data injection (FDI) attacks. We have examined generic linear leader-following agents in the context of stochastic FDI attacks on the network topology. When information is sent from one agent to another, it is altered as a result of the attacks.
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