Distributed Optimisation with Stochastic Event-Triggered Multi-Agent Control Algorithm

In this paper, we study the distributed optimisation problem in which multiple agents cooperatively and distributively solve an optimisation problem. In order to avoid continuous communication among agents, we propose a stochastic distributed dynamic event-triggering law to schedule the communication. We show that the optimisation problem can be solved with exponential rate and arbitrarily small optimisation error. We further prove that Zeno behaviour does not exist in the proposed stochastic event-triggering law by constructing a lower bound on the inter-event interval which is essential for the feasibility of proposed algorithm. A numerical simulation is presented to illustrate the effectiveness of the proposed algorithm when compared with some existing event-triggered distributed optimisation algorithms.

[1]  Ling Shi,et al.  Stochastic event-triggered sensor scheduling for remote state estimation , 2013, 52nd IEEE Conference on Decision and Control.

[2]  Ali H. Sayed,et al.  Exact Diffusion for Distributed Optimization and Learning—Part I: Algorithm Development , 2017, IEEE Transactions on Signal Processing.

[3]  Asuman E. Ozdaglar,et al.  Distributed Subgradient Methods for Multi-Agent Optimization , 2009, IEEE Transactions on Automatic Control.

[4]  Daniel E. Quevedo,et al.  Event-Triggered Quantized Communication-Based Distributed Convex Optimization , 2018, IEEE Transactions on Control of Network Systems.

[5]  Wei Ren,et al.  Event-triggered zero-gradient-sum distributed consensus optimization over directed networks , 2016, Autom..

[6]  Ziyang Meng,et al.  Sampled-Data Consensus Over Random Networks , 2015, IEEE Transactions on Signal Processing.

[7]  Junfeng Wu,et al.  Zeno-Free Stochastic Distributed Event-Triggered Consensus Control for Multi-Agent Systems , 2019, 2019 American Control Conference (ACC).

[8]  Karl Henrik Johansson,et al.  Distributed Optimization with Dynamic Event-Triggered Mechanisms , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[9]  Choon Yik Tang,et al.  Zero-Gradient-Sum Algorithms for Distributed Convex Optimization: The Continuous-Time Case , 2011, IEEE Transactions on Automatic Control.

[10]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[11]  Karl Henrik Johansson,et al.  Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[12]  Karl Henrik Johansson,et al.  Distributed dynamic event-triggered control for multi-agent systems , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[13]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[14]  Asuman E. Ozdaglar,et al.  Convergence rate for consensus with delays , 2010, J. Glob. Optim..

[15]  Tomohisa Hayakawa,et al.  Stochastic communication protocols for multi-agent consensus under jamming attacks , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[16]  Angelia Nedic,et al.  Distributed optimization over time-varying directed graphs , 2013, 52nd IEEE Conference on Decision and Control.

[17]  Wei Shi,et al.  A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates , 2017, IEEE Transactions on Signal Processing.

[18]  Albert S. Berahas,et al.  Balancing Communication and Computation in Distributed Optimization , 2017, IEEE Transactions on Automatic Control.

[19]  Xuhui Bu,et al.  Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control , 2018, IEEE Transactions on Neural Networks and Learning Systems.