Chunjie Yang

Observer-based event-triggered containment control of multi-agent systems with time delay

ABSTRACT This paper studies the containment control of general linear multi-agent systems with or without time delay. The observer-based event-triggered control schemes will be considered. For the conventional distributed containment control protocol, we will not update the relative state continuously, i.e. the relative state will be updated by some events which happen intermittently. A completely decentralised event trigger will be designed for leader–follower systems. Under the proposed protocol, if we design some appropriate feedback gain matrices, all followers will asymptotically converge to the convex hull spanned by the dynamic leaders. Numerical simulations are also provided and the results show highly consistent with the theoretical results.

Observer-based event-triggered control for consensus of multi-agent systems with time delay

This paper studies the consensus of general linear multi-agent systems with or without time delays. The observer-based event-triggered control schemes will be considered. For the conventional distributed consensus protocol, we will not update the relative state in continuous time, i.e., the relative state will be updated by some events which happened in discrete time. A completely decentralized event-trigger will be designed for multi-agent systems. It is shown that all agents can achieve consensus by the proposed protocol, if we design some appropriate feedback gain matrices. Numerical simulations are also provided and the results show highly consistent with the theoretical results.

Synchronization of nonlinear multi-agent systems under measurement noises and external disturbance

This paper studies the synchronization problem with a guaranteed H∞ performance for nonlinear multiagent systems. It considers a practical situation: the measurement of relative-state is disturbed and each agent has external disturbance. We show that all agents can achieve synchronization in mean square and meanwhile maintain a desirable disturbance rejection performance by a consensus protocol. Numerical simulations are also provided and the results show highly consistent with the theoretical results.

Comparison of univariate and multivariate predicted method based on support vector regression for silicon content in hot metal

Silicon content plays an important role in reflecting the thermal state of a blast furnace (BF). The commonly used methods to predict silicon content at present are univariate and multivariate predicted method. The univariate predicted method only uses the information of silicon content in hot metal and can not reflect the complexity of blast furnace ironmaking process. The multivariate predicted method takes advantage of the rich information in blast furnace, different variables usually have a strong correlation with each other, which is not only useless for prediction but also brings in a lot of noise. So it always can not obtain the special satisfactory results. In this article, we have collected the real data from a medium-size BF and done three experiments based on support vector regression. These experiments can divide into two categories, univariate predicted method (UPM) and multivariate predicted method (MPM), the latter includes local variables prediction (LVP) and whole variables prediction (WVP). Multivariate correlation analysis and Spearmans rank correlation coefficient are applied to select variables which have low correlation for LVP. Phase space reconstruction is used to obtain the input and output of UPM. The experiment results show that LVP is better than the UPM and WVP. This also powerfully illustrates that choosing more variables can indeed bring in more useful information for prediction, especially when the correlation is eliminated through the selection of variables, the prediction result will be better.