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RHC-Based Consensus of Multi-Agent Systems with Simultaneous Packet Dropout and Input Delay

XU Juanjuan1, ZHANG Zhaorong2   

  1. 1. School of Control Science and Engineering, Shandong University, Jinan 250061, China;
    2. School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia
  • Received:2020-10-19 Revised:2021-06-24 Online:2022-08-25 Published:2022-08-02
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61633014, 61922051, U1806204, 61873332, U1701264, the Foundation for Innovative Research Groups of National Natural Science Foundation of China under Grant No. 61821004, and Youth Innovation Group Project of Shandong University under Grant No. 2020QNQT016, Science and Technology Project of Qingdao West Coast New Area (2019-32, 2020-20, 2020-1-4), High-level Talent Team Project of Qingdao West Coast New Area (RCTD-JC-2019- 05) and Key Research and Development Program of Shandong Province under Grant No. 2020CXGC01208.

XU Juanjuan, ZHANG Zhaorong. RHC-Based Consensus of Multi-Agent Systems with Simultaneous Packet Dropout and Input Delay[J]. Journal of Systems Science and Complexity, 2022, 35(4): 1262-1277.

This paper is concerned with the multi-agent systems with both packet dropout and input delay. A novel receding horizon control (RHC) based consensus protocol is proposed by solving a distributed RHC based optimization problem. The novelty of the optimization problem lines in the involvement of the neighbours' predictor information in the cost functions. Based on the derived RHC based consensus protocol, the necessary and sufficient condition for the mean-square consensus is obtained. In addition, the authors give a specific sufficient condition to guarantee the mean-square consensus.
[1] Godsil C and Royle G, Algebraic Graph Theory, Springer, New York, 2001.
[2] Fax J A and Murray R M, Information flow and cooperative control of vehicle formations, IEEE Transactions on Automatic Control, 2004, 49(9): 1465-1476.
[3] Lynch N A, Distributed Algorithms, Morgan Kaufmann, Waltham, MA, 1996.
[4] Cortés J and Bullo F, Coordination and geometric optimization via distributed dynamical systems, SIAM Journal on Control and Optimization, 2006, 44(5): 1543-1574.
[5] Ma C Q and Zhang J F, Necessary and sufficient conditions for consensusability of linear multiagent systems, IEEE Transactions on Automatic Control, 2010, 55(5): 1263-1268.
[6] You K Y and Xie L H, Network topology and communication data rate for consensusability of discrete-time multi-agent systems, IEEE Transactions on Automatic Control, 2011, 56(10): 2262-2275.
[7] Olfati-Saber R and Murray R M, Consensus problems in networks of agents with switching topology and time-delays, IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533.
[8] Lin P and Ren W, Constrained consensus in unbalanced networks with communication delays, IEEE Transactions on Automatic Control, 2014, 59: 775-781.
[9] Xu J J, Zhang H S, and Xie L H, Input delay margin for consensusability of multi-agent systems, Automatica, 2013, 49: 1816-1820.
[10] Zhou B and Lin Z L, Consensus of high-order multi-agent systems with large input and communication delays, Automatica, 2014, 50(2): 452-464.
[11] Li T and Zhang J F, Mean square average-consensus under measurement noises and fixed topologies: Necessary and sufficient conditions, Automatica, 2009, 45(8): 1929-1936.
[12] Tang H B and Li T, Continuous-time stochastic consensus: Stochastic approximation and Kalman-Bucy filtering based protocol, Automatica, 2015, 61: 146-155.
[13] Ni Y H and Li X, Consensus seeking in multi-agent systems with multiplicative measurement noises, Systems and Control Letters, 2013, 62: 430-437.
[14] Li T, Wu F K, and Zhang J F, Multi-agent consensus with relative state-dependent measurement noises, IEEE Transactions on Automatic Control, 2014, 59(9): 2463-2468.
[15] Cao Y C and Ren W, Optimal linear-consensus algorithms: An LQR perspective, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2010, 40(3): 819-829.
[16] Bauso D, Giarr L, and Pesenti R, Nonlinear protocols for optimal distributed consensus in networks of dynamics agents, Systems and Control Letters, 2006, 55(11): 918-928.
[17] Zhang H G, Feng T, Yang G H, et al., Distributed cooperative optimal control for multiagent systems on directed graphs: An inverse optimal approach, IEEE Transactions on Cybernetics, 2015, 45(7): 1315-1326.
[18] Gupta V, Hassibi B, and Murray R M, A sub-optimal algorithm to synthesize control laws for a network of dynamic agents, International Journal of Control, 2005, 78(16): 1302-1313.
[19] Nguyen D H, A sub-optimal consensus design for multi-agent systems based on hierarchical LQR, Automatica, 2015, 55: 88-94.
[20] Tan C and Zhang H S, Necessary and sufficient stabilizing conditions for networked control systems with simultaneous transmission delay and packet dropout, IEEE Transactions on Automatic Control, 2017, 62(8): 4011-4016.
[21] Mayne D Q, Model predictive control: Recent developments and future promise, Automatica, 2014, 50(12): 2967-2986.
[22] Mayne D Q, Rawlings J B, Rao C V, et al., Constrained model predictive control: Stability and optimality, Automatica, 2000, 36(6): 789-814.
[23] Kwon W H, Lee Y S, and Han S H, General receding horizon control for linear time-delay systems, Automatica, 2004, 40(9): 1603-1611.
[24] Park J H, Yoo H W, Han S, et al., Receding horizon control for input delayed systems, IEEE Transactions on Automatic Control, 2008, 53(7): 1746-1752.
[25] Gao R, Xu J J, and Zhang H S, Receding horizon control for multiplicative noise stochastic systems with input delay, Automatica, 2017, 81: 390-396.
[26] Dunbar W B and Murray R M, Distributed receding horizon control for multi-vehicle formation stabilization, Automatica, 2006, 42(4): 549-558.
[27] Müller M A, Reble M, and Allgöwer F, Cooperative control of dynamically decoupled systems via distributed model predictive control, International Journal of Robust and Nonlinear Control, 2004, 22(12): 1376-1397.
[28] Keviczky T, Borrelli F, and Balas G J, Decentralized receding horizon control for large scale dynamically decoupled systems, Automatica, 2006, 42(12): 2105-2115.
[29] Li H P and Yan W S, Receding horizon control based consensus scheme in general linear multiagent systems, Automatica, 2015, 56: 12-18.
[30] Zhang W A and Yu L, Modelling and control of networked control systems with both networkinduced delay and packet dropout, Automatica, 2008, 44(12): 3206-3210.
[31] Zong X F, Li T, Yin G, et al., Stochastic consentability of linear systems with time delays and multiplicative noises, IEEE Transactions on Automatic Control, 2018, 63(4): 1059-1074.
[32] Xu J J, Zhang H S, and Xie L H, Consensusability of multiagent systems with delay and packet dropout under predictor-like protocols, IEEE Transactions on Automatic Control, 2019, 64(8): 3506-3513.
[33] Zhang H S, Li L, Xu J J, et al., Linear quadratic regulation and stabilization of discrete-time systems with delay and multiplicative noise, IEEE Transactions on Automatic Control, 2015, 60(10): 2599-2613.
[34] Zhang H S and Xu J J, Control for Itô stochastic systems with input delay, IEEE Transactions on Automatic Control, 2017, 62(1): 350-365.
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