Minimize the Prediction Error in External Consensus Problem using Gain Error Ratio Formula

Nurul Adilla Mohd Subha, Mohd Ariffanan Mohd Basri, Mohamad Amir Shamsudin


This paper discusses the external consensus problem for non-identical networked multi-agent systems (NMAS) with network data loss, considering uniform consecutive data losses (CDL) induced by long periods of transmission failure. A gain error ratio (GER) formula is proposed to determine the appropriate value of coupling gain between agents in order to minimize the computed prediction error caused by the prediction process. Consequently, the consensus performance with prediction control strategy can be improved. The effectiveness of the proposed formula is demonstrated through simulation.


Consecutive data losses; consensus; coupling gain; multi-agent system; prediction

Article Metrics

Abstract view : 175 times
PDF - 42 times

Full Text:



A. Eichler and H. Werner, Closed-form solution for optimal convergence speed of multi-agent systems with discrete-time double integrator dynamics, System Control Letters, 71, 7-13, 2014.

J. Zhu, Y. P. Tian and J. Kuang, On the general consensus protocol of multi-agent systems with double-integrator dynamics, Linear Algebra Application, 701-715, 2009.

M. Yu, L. Li and G. Xie, Average consensus in multiagent systems with the problem of packet losses when using the second-order neighbors’ information, Mathematical Problems in Engineering, Article ID 304126, 2014.

F. Fagnani and S. Zampieri, Average consensus with packet dropout communication, SIAM Journal on Control and Optimization, 48(1), 102-133, 2009.

A. D. Dominguez, C. N. Hadjicostis and N. H. Vaidya, Distributed algorithm for consensus and coordination in the presence of packet-dropping communication links, Coordinated Science Laboratory Technical Report, September, 2011.

N. A. Mohd Subha and G. P. Liu, External Consensus in multi-agent systems with large consecutive data loss under unreliable networks, May, 2016.


  • There are currently no refbacks.