Abstract
In a multiagent system (MAS), communication signals are affected by harsh wireless networks when they are transmitted from an agent to its neighboring agents, leading to the inconsistency of the MAS. In this paper, an average-iterative learning control (average-ILC) method is studied to address the consensus problem of MAS over wireless networks in the presence of channel noise and data dropout. The combined effects of channel noise and data dropout on iterative learning controllers are carefully analyzed. Based on graph theory and mathematical expectation, the corresponding average-iterative learning scheme is proposed. Especially, a sufficient condition is derived for the average-iterative learning scheme. Rigorous theoretical analysis demonstrates that the convergence of the covariance matrix of tracking error can be guaranteed with the help of an average-iterative learning scheme. Finally, simulation results are given to show the effectiveness of the proposed method.
Highlights
E applications of ILC in multiagent frameworks have been developed for many years
For multiagent system (MAS), all agents are usually located in different sites and communicate with others via wireless networks. en, channel noise and data dropout always exist in channels of wireless networks and cause interference to the transmitted signals. us, reliable transmission is hard to be realized in actual networks when there are channel noise and data dropout in the wireless communication channel. is increases the design difficulty of iterative learning controllers
The signal cannot be received if data dropout exists; otherwise, the signal is received by other agents in an imprecise form. en, these combined effects of channel noise and data dropout act on the iterative learning controller, causing the ILC algorithms to fail to converge. is indicates that the design complexity of the iterative learning controller is greatly increased when channel noise and data dropout coexist
Summary
⎧⎪⎨ 1, vi, vj ∈ E, aji ⎪⎩ 0, vi, vj ∉ E or j i. Channel noise and data dropout in wireless networks can cause interference to transmitted signals. In this figure, agents 1, 2, and 3 are neighboring agents of agent j. E controller obtains the mixing error ξj,k(t), which contains the randomness of channel noise and data dropout. It assumes that Rm and μ are known to us because the statistics of both channel noise and data dropout rely on the actual environment.
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