Stochastic Bounded Consensus Tracking of Second-Order Multi-Agent Systems with Measurement Noises and Sampled-Data

  • Authors:

  • Zhihai Wu;Li Peng;Linbo Xie;Jiwei Wen

  • Affiliations:

  • Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education, School of Internet of Things Engineering, Jiangnan University, Wuxi, People's Republic of China 214122;Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education, School of Internet of Things Engineering, Jiangnan University, Wuxi, People's Republic of China 214122 a ...;Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education, School of Internet of Things Engineering, Jiangnan University, Wuxi, People's Republic of China 214122;Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education, School of Internet of Things Engineering, Jiangnan University, Wuxi, People's Republic of China 214122

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 2012

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Abstract

This paper investigates the stochastic bounded consensus tracking problems of second-order multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbors or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory, the algebra graph theory, and some other techniques are employed to derive the necessary and sufficient condition guaranteeing the mean square bounded consensus tracking. It turns out that the maximum allowable sampling period depends on not only the network topology but also the constant feedback gains. Furthermore, the effects of the sampling period on tracking performance, including the tracking speed and the static tracking error, are also analyzed. The results show that reducing the sampling period can accelerate the tracking speed and decrease the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.