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Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
G.-P. Liu,  “Tracking control of multi-agent systems using a networked predictive PID tracking scheme,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 216–225, Jan. 2023. doi: 10.1109/JAS.2023.123030
Citation: G.-P. Liu,  “Tracking control of multi-agent systems using a networked predictive PID tracking scheme,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 216–225, Jan. 2023. doi: 10.1109/JAS.2023.123030

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

doi: 10.1109/JAS.2023.123030
Funds:  This work was supported in part by the National Natural Science Foundation of China (62173255, 62188101)
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  • With the rapid development of network technology and control technology, a networked multi-agent control system is a key direction of modern industrial control systems, such as industrial Internet systems. This paper studies the tracking control problem of networked multi-agent systems with communication constraints, where each agent has no information on the dynamics of other agents except their outputs. A networked predictive proportional integral derivative (PPID) tracking scheme is proposed to achieve the desired tracking performance, compensate actively for communication delays, and simplify implementation in a distributed manner. This scheme combines the past, present and predictive information of neighbour agents to form a tracking error signal for each agent, and applies the proportional, integral, and derivative of the agent tracking error signal to control each individual agent. The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems. The effectiveness of the networked PPID tracking scheme is illustrated via an example.


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    • A networked predictive PID tracking scheme is proposed to achieve the desired tracking performance for multi-agent systems
    • The scheme actively compensates for communication delays
    • The proposed scheme is simply implemented in a distributed way so that each agent does not need to have information on the dynamics of its neighbor agents
    • The scheme largely simplifies the parameter tuning and implementation with much less computing load


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