Synchronization with partial state coupling on SO(n)

This paper studies autonomous synchronization of k agents whose states evolve on SO(n), but which are only coupled through the action of their states on one "reference vector" in Rn for each link. Thus each link conveys only partial state information at each time, and to reach synchronization agents must combine this information over time or throughout the network. A natural gradient coupling law for synchronization is proposed. Extensive convergence analysis of the coupled agents is provided, both for fixed and time-varying reference vectors. The case of SO(3) with fixed reference vectors is discussed in more detail. For comparison, we also treat the equivalent setting in Rn, i.e. with states in Rn and connected agents comparing scalar product of their states with a reference vector.Comment: to be submitted to SIAM Journal on Control and Optimizatio

Observers for invariant systems on Lie groups with biased input measurements and homogeneous outputs

This paper provides a new observer design methodology for invariant systems whose state evolves on a Lie group with outputs in a collection of related homogeneous spaces and where the measurement of system input is corrupted by an unknown constant bias. The key contribution of the paper is to study the combined state and input bias estimation problem in the general setting of Lie groups, a question for which only case studies of specific Lie groups are currently available. We show that any candidate observer (with the same state space dimension as the observed system) results in non-autonomous error dynamics, except in the trivial case where the Lie-group is Abelian. This precludes the application of the standard non-linear observer design methodologies available in the literature and leads us to propose a new design methodology based on employing invariant cost functions and general gain mappings. We provide a rigorous and general stability analysis for the case where the underlying Lie group allows a faithful matrix representation. We demonstrate our theory in the example of rigid body pose estimation and show that the proposed approach unifies two competing pose observers published in prior literature.Comment: 11 page

Synchronization with partial state feedback on SO(n),

peer reviewedThis paper considers the problem of constructing a distributed feedback law to achieve synchronization for a group of k agents whose states evolve on SO(n) and which exchange only partial state information along communication links. The partial state information is given by the action of the state on reference vectors in Rn. We propose a gradient based control law which achieves exponential local convergence to a synchronization configuration under a rank condition on a generalized Laplacian matrix. Furthermore, we discuss the case of time-varying reference vectors and provide a convergence result for this case. The latter helps reach synchronization, requiring less communication links and weaker conditions on the instantaneous reference vectors. Our methods are illustrated on an attitude synchronization problem where agents exchange only their relative positions observed in the respective body frames

Decompounding on compact Lie groups

Noncommutative harmonic analysis is used to solve a nonparametric estimation problem stated in terms of compound Poisson processes on compact Lie groups. This problem of decompounding is a generalization of a similar classical problem. The proposed solution is based on a char- acteristic function method. The treated problem is important to recent models of the physical inverse problem of multiple scattering.Comment: 26 pages, 3 figures, 25 reference

An Arrow-Hurwicz-Uzawa type flow as least squares solver for network linear equations

We study the approach to obtaining least squares solutions to systems of linear algebraic equations over networks by using distributed algorithms. Each node has access to one of the linear equations and holds a dynamic state. The aim for the node states is to reach a consensus as a least squares solution of the linear equations by exchanging their states with neighbors over an underlying interaction graph. A continuous-time distributed least squares solver over networks is developed in the form of the famous Arrow–Hurwicz–Uzawa flow. A necessary and sufficient condition is established on the graph Laplacian for the continuous-time distributed algorithm to give the least squares solution in the limit, with an exponentially fast convergence rate. The feasibility of different fundamental graphs is discussed including path graph and random graph. Moreover, a discrete-time distributed algorithm is developed by Euler’s method, converging exponentially to the least squares solution at the node states with suitable step size and graph conditions.This work was supported by the DAAD with funds of the German Federal Ministry of Education and Research (BMBF), by the Australian Research Council (ARC) under grants DP-130103610 and DP-160104500

Pointwise convergence of gradient-like systems

S. Łojasiewicz has shown that the ω-limit sets of the trajectories of analytic gradient systems consist of at most one point. We extend this result to the larger class of gradient-like vector fields satisfying an angle condition. In particular, this in

Konvergenz gradientenähnlicher dynamischer Systeme und Optimierungsalgorithmen

This work studies the convergence of trajectories of gradient-like systems. In the first part of this work continuous-time gradient-like systems are examined. Results on the convergence of integral curves of gradient systems to single points of Lojasiewicz and Kurdyka are extended to a class of gradient-like vector fields and gradient-like differential inclusions. In the second part of this work discrete-time gradient-like optimization methods on manifolds are studied. Methods for smooth and for nonsmooth optimization problems are considered. For these methods some convergence results are proven. Additionally the optimization methods for nonsmooth cost functions are applied to sphere packing problems on adjoint orbits.Diese Arbeit beschäftigt sich mit der Konvergenz von Trajektorien gradientenähnlicher Systeme. Im ersten Teil der Arbeit werden zeit-kontinuierliche, gradientenähnliche Systeme betrachtet. Resultate zur Konvergenz der Trajektorien gegen einen Punkt von Lojasiewicz und Kurdyka für Gradientensysteme werden auf eine Klasse gradientenähnlicher Vektorfelder und gradientenähnliche Differentialinklusionen verallgemeinert. Im zweiten Teil der Arbeit werden zeit-diskrete, gradientenähnliche Optimierungsverfahren auf Mannigfaltigkeiten untersucht. Es werden Algorithmen sowohl für glatte als auch nicht-glatte Optimierungsprobleme betrachtet. Für diese Verfahren werden einige Konvergenzresultate bewiesen. Zusätzlich werden die Optimierungsverfahren für nichtglatte Kostenfunktionen auf Kugelpackungsprobleme in adjungierten Bahnen angewendet

Gradient-Like Observers for Invariant Dynamics on a Lie Group

This paper proposes a design methodology for non-linear state observers for invariant kinematic systems posed on finite dimensional connected Lie groups, and studies the associated fundamental system structure. The concept of synchrony of two dynamical systems is specialized to systems on Lie groups. For invariant systems this leads to a general factorization theorem of a nonlinear observer into a synchronous (internal model) term and an innovation term. The synchronous term is fully specified by the system model. We propose a design methodology for the innovation term based on gradient-like terms derived from invariant or non-invariant cost functions. The resulting nonlinear observers have strong (almost) global convergence properties and examples are used to demonstrate the relevance of the proposed approach