Controllability of continuum ensemble of formation systems over directed graphs

We propose in the paper a novel framework for using a common control input to simultaneously steer an infinite ensemble of networked control systems. We address the problem of co-designing information flow topology and network dynamics of every individual networked system so that a continuum ensemble of such systems is controllable. To keep the analysis tractable, we focus in the paper on a special class of ensembles systems, namely ensembles of multi-agent formation systems. Specifically, we consider an ensemble of formation systems indexed by a parameter in a compact, real analytic manifold. Every individual formation system in the ensemble is composed of $N$ agents. These agents evolve in $\mathbb{R}^n$ and can access relative positions of their neighbors. The information flow topology within every individual formation system is, by convention, described by a directed graph where the vertices correspond to the $N$ agents and the directed edges indicate the information flow. For simplicity, we assume in the paper that all the individual formation systems share the same information flow topology described by a common digraph $G$. Amongst other things, we establish a sufficient condition for approximate path-controllability of the continuum ensemble of formation systems. We show that if the digraph $G$ is strongly connected and the number $N$ of agents in each individual system is great than $(n + 1)$, then every such system in the ensemble is simultaneously approximately path-controllable over a path-connected, open dense subset.

[1]  John Baillieul,et al.  Information patterns and Hedging Brockett's theorem in controlling vehicle formations , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[2]  Xudong Chen,et al.  Structure theory for ensemble controllability, observability, and duality , 2018, Mathematics of Control, Signals, and Systems.

[3]  Magnus Egerstedt,et al.  Controllability of Multi-Agent Systems from a Graph-Theoretic Perspective , 2009, SIAM J. Control. Optim..

[4]  Andreas Boukas,et al.  Structure and decompositions of the linear span of generalized stochastic matrices , 2015 .

[5]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[6]  Tamer Basar,et al.  Controllability of Formations Over Directed Time-Varying Graphs , 2017, IEEE Transactions on Control of Network Systems.

[7]  Jr-Shin Li,et al.  Control and Synchronization of Neuron Ensembles , 2011, IEEE Transactions on Automatic Control.

[8]  Andrei A. Agrachev,et al.  Ensemble controllability by Lie algebraic methods , 2016 .

[9]  Wensheng Liu,et al.  An Approximation Algorithm for Nonholonomic Systems , 1997 .

[10]  Jr-Shin Li,et al.  Ensemble Control of Bloch Equations , 2009, IEEE Transactions on Automatic Control.

[11]  S. Datta,et al.  Proposal for an all-spin logic device with built-in memory. , 2010, Nature nanotechnology.

[12]  T. Ikeda,et al.  Photomechanics: Directed bending of a polymer film by light , 2003, Nature.

[13]  H. Bergman,et al.  Pathological synchronization in Parkinson's disease: networks, models and treatments , 2007, Trends in Neurosciences.

[14]  Richard M. Murray,et al.  Steering Nonholonomic Control Systems Using Sinusoids , 1993 .

[15]  Magnus Egerstedt,et al.  The degree of nonholonomy in distributed computations , 2014, 53rd IEEE Conference on Decision and Control.

[16]  Demetris K. Roumis,et al.  Coordinated Excitation and Inhibition of Prefrontal Ensembles during Awake Hippocampal Sharp-Wave Ripple Events , 2016, Neuron.

[17]  Shreyas Sundaram,et al.  Structural Controllability and Observability of Linear Systems Over Finite Fields With Applications to Multi-Agent Systems , 2013, IEEE Transactions on Automatic Control.

[18]  H. Sussmann,et al.  Lie Bracket Extensions and Averaging: The Single-Bracket Case , 1993 .

[19]  S. Glaser,et al.  Unitary control in quantum ensembles: maximizing signal intensity in coherent spectroscopy , 1998, Science.

[20]  Laura Waller,et al.  Precise multimodal optical control of neural ensemble activity , 2018, Nature Neuroscience.

[21]  Ian Appelbaum,et al.  Spin-Polarization Control in a Two-Dimensional Semiconductor , 2016, 1601.07527.

[22]  Toru Asahi,et al.  Walking and rolling of crystals induced thermally by phase transition , 2018, Nature Communications.