Flocking of Mobile Agents Using a New Interaction Model: A Cyber-Physical Perspective

The attraction repulsion (AR) model has been a common mathematical framework to emulate the interactions among mobile agents and to design rigid flocking laws. A main drawback of the AR model is that environment effects are not taken into account in the model. This means that rigid flocking laws can not react to the change of environments. In this paper, we make an attempt to design flocking systems that are adaptive to the change of communication environments. The flocking system is modeled as a cyber-physical system, where the cyber layer and control layer are designed systematically. In the cyber layer, a new interaction model is proposed by considering communication parameters of the environment. In the control layer, distributed controllers are designed for mobile agents with switching topology using the proposed interaction model. It is shown that the proposed flocking law can react to change of communication environments and guarantee the optimal communication link between agents. The stability and convergence of the flocking system are analyzed with nonsmooth techniques. Numerical simulations are provided to illustrate the effectiveness of the design.

[1]  Guanrong Chen,et al.  Model predictive flocking control for second-order multi-agent systems with input constraints , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Ling Shi,et al.  Time synchronization in WSNs: A maximum value based consensus approach , 2011, IEEE Conference on Decision and Control and European Control Conference.

[3]  Michael M. Zavlanos,et al.  Distributed communication-aware coverage control by mobile sensor networks , 2016, Autom..

[4]  Helen Gill,et al.  Cyber-Physical Systems , 2019, 2019 IEEE International Conference on Mechatronics (ICM).

[5]  Radhika Nagpal,et al.  Programmable self-assembly in a thousand-robot swarm , 2014, Science.

[6]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[7]  Shouchuan Hu.  Differential equations with discontinuous right-hand sides☆ , 1991 .

[8]  Victor C. M. Leung,et al.  Toward Offering More Useful Data Reliably to Mobile Cloud From Wireless Sensor Network , 2015, IEEE Transactions on Emerging Topics in Computing.

[9]  A. Dornhaus,et al.  When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents , 2015 .

[10]  George J. Pappas,et al.  Flocking in Fixed and Switching Networks , 2007, IEEE Transactions on Automatic Control.

[11]  Gordon F. Royle,et al.  Algebraic Graph Theory , 2001, Graduate texts in mathematics.

[12]  Ahmed Rahmani,et al.  Leader-follower formation control of nonholonomic mobile robots based on a bioinspired neurodynamic based approach , 2013, Robotics Auton. Syst..

[13]  Mary Ann Weitnauer,et al.  Distributed extremum seeking and formation control for nonholonomic mobile network , 2015, Syst. Control. Lett..

[14]  George J. Pappas,et al.  Stable flocking of mobile agents part I: dynamic topology , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[15]  Janina Maier.  On The Move How And Why Animals Travel In Groups , 2016 .

[16]  Mounir Ghogho,et al.  Mobility Diversity-Assisted Wireless Communication for Mobile Robots , 2016, IEEE Transactions on Robotics.

[17]  Ahmed Rahmani,et al.  Distributed consensus-based formation control for multiple nonholonomic mobile robots with a specified reference trajectory , 2015, Int. J. Syst. Sci..

[18]  Chao Zhai,et al.  A General Alignment Repulsion Algorithm for Flocking of Multi-Agent Systems , 2011, IEEE Transactions on Automatic Control.

[19]  David Hu,et al.  Mechanics of fire ant aggregations. , 2016, Nature materials.

[20]  James G. Puckett,et al.  Intrinsic fluctuations and driven response of insect swarms. , 2015, Physical review letters.

[21]  Jie Zhang,et al.  Reconfiguring active particles by electrostatic imbalance. , 2016, Nature materials.

[22]  F. Clarke.  Optimization And Nonsmooth Analysis , 1983 .

[23]  Panganamala Ramana Kumar,et al.  Cross-layer design for cyber-physical systems of coordinated networked vehicles over bi-directional middleware , 2016, 2016 American Control Conference (ACC).

[24]  Zhiwu Huang,et al.  A novel communication-aware formation control strategy for dynamical multi-agent systems , 2015, J. Frankl. Inst..

[25]  Wei Ren,et al.  Consensus based formation control strategies for multi-vehicle systems , 2006, 2006 American Control Conference.

[26]  Yilun Shang,et al.  Finite-time consensus for multi-agent systems with fixed topologies , 2012, Int. J. Syst. Sci..

[27]  Victor C. M. Leung,et al.  A Survey on Mobile Social Networks: Applications, Platforms, System Architectures, and Future Research Directions , 2015, IEEE Communications Surveys & Tutorials.

[28]  B. Paden,et al.  Lyapunov stability theory of nonsmooth systems , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[29]  Y. Shang.  L1 group consensus of multi-agent systems with switching topologies and stochastic inputs , 2013 .

[30]  Victor C. M. Leung,et al.  Vita: A Crowdsensing-Oriented Mobile Cyber-Physical System , 2013, IEEE Transactions on Emerging Topics in Computing.