Distributed Estimation for Motion Coordination in an Unknown Spatially Varying Flowfield

Peterson, Carla L.; Paley, Derek A.

doi:10.2514/1.59453

Cited by 27 publications

(15 citation statements)

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“…Even in the formation control, 32,33 a single task, the cooperative control with switching topologies is more challenging in comparison with the fixed cases. Distinguishing from the existing results based on fixed bidirectional networks, [19][20][21][22]29,36 this paper is the first attempt to access to the problem of spherical formation tracking control in unknown flowfields with switching directed topologies and our proposed control laws as well as the flow observers also do not require any global information of each directed topology, which is the third contribution of this paper. Different from the differential Lyapunov-based method used in the works of Dong et al, 32,33 the Lyapunov functions constructed in this paper are nondifferential with deriving from the time-varying left eigenvector of Lapalacian matrix associated with each switching topology.…”

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confidence: 91%

“…It is worth noting that the control objectives in this paper contain the tracking of sphere and circle, the lateral formation, the flow estimation, and the avoidance of each agent's trajectory to Earth's axis due to the undefined lateral formation, a multigoal tasks, which is more complicated and difficult to be controlled than the tradition formation control problem [30][31][32][33] and the single consensus problem under the directed strongly connected topology. 34,35 The second contribution of this paper is to present a new robust spherical formation tracking control law with flow estimates that do not used any global information of network (eg, the left eigenvector of Lapalacian matrix) and applicable to almost all flow forms with uncertain parameter vectors including the velocity field (eg, the constant-velocity flowfield, 19 the rotating flowfield, 20 the parameterized flowfield, 21 and the Eulerian specification flowfields 22,29,36 ) and the gravitational fields. 37,38 In this paper, we introduce adaptive backstepping to construct two kinds of observers (say, the second-order observer and the minimum-order observer) for the velocity field and an adaptive update law for the estimation of the gravitational field.…”

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confidence: 99%

“…Moreover, the estimation of gravitational field given in this paper is not involved in related works. [19][20][21][22]36 Nobody could fail to notice the fact that the network among agents may be switching due to the existence of communication links failures and reestablish connection again. Even in the formation control, 32,33 a single task, the cooperative control with switching topologies is more challenging in comparison with the fixed cases.…”

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confidence: 99%

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Spherical formation tracking control for second‐order agents with unknown general flowfields and strongly connected topologies

Chen

Zhang

2019

Intl J Robust & Nonlinear

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This article addresses the problem of directing a family of second-order agents suffering external flowfields to achieve the lateral formation tracking motion on a target sphere. Distinguishing from the existing results based on bidirectional networks, this paper firstly attempts to deal with the fixed directed strongly connected multiagent systems and then the switch directed networks with the strong connectivity of each topology. Both the velocity field (eg, the constant-velocity flowfield, the rotating flowfield, Eulerian specification flowfield, the parameterized flowfield) and the gravitational field are under consideration, where the flow specification is a spatiotemporal variable with an unknown parameter vector. Therefore, it is known as the general flowfield. To access to the fixed network, a new second-order observer for the velocity field as well as an adaptive estimate for the gravitational field are constructed by using the tool of adaptive backstepping in the beginning. They, together with the distributed control laws in the spherical normal, lateral, and longitudinal directions, are proposed to accomplish spherical tracking, circular tracking, and lateral formation.For the purpose of avoiding the overparametrization of observer and reducing the complexity of design, a minimum-order observer is proposed later. Finally, our proposed methods servicing to the fixed topologies are developed to the cases where the switching topologies are directed and each one is strongly connected.The stability of the fixed and directed strongly connected system is investigated based on the Barbalat's lemma, whereas the Lyapunov stability theory of nonsmooth systems is introduced to analyze the stability of the switching cases. Theoretical results are proven by the numerical examples. KEYWORDS adaptive backstepping, minimum-order observer, spatiotemporal flowfield, spherical formation tracking control, switching directed topology 1 INTRODUCTION Currently, with the development of the standard of sensor, an increasing number of applications in oceanographic explorations, 1,2 planet explorations, 3,4 and the like have been executed by mobile sensor networks. To maximize data Int J Robust Nonlinear Control. 2019;29:3715-3736.wileyonlinelibrary.com/journal/rnc

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confidence: 99%

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confidence: 99%

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Spherical formation tracking control for second‐order agents with unknown general flowfields and strongly connected topologies

Chen

Zhang

2019

Intl J Robust & Nonlinear

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“…Distributed estimation technology has been commonly used in process control, signal processing, and information systems. [15][16][17][18] A subset of these efforts has generally been focused on the integration of measurements from all the sensors into a common estimate without using a centralized processor. For example, the work of Olfati-Saber 19 has contributed a great deal of work toward achieving an average consensus among distributed filters.…”

Section: Introductionmentioning

confidence: 99%

Distributed estimation for spatial rigid motion based on dual quaternions

Lee

Dai

2018

Optim Control Appl Methods

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Summary This paper proposes 2 distributed optimization algorithms for the estimation of spatial rigid motion using multiple image sensors in a connected network. The objective is to increase the estimation precision of translational and rotational motion based on dual quaternion models and the cooperation between connected sensors. The dual decomposition subgradient method and distributed Newton optimization method are applied to decompose the filtering task into a series of suboptimal problems and then solve them individually to achieve the global optimality. Our approach assumes that each sensor can communicate with its neighboring sensors to update the individual estimates. Discussion on converging speed of both methods are provided. Simulation examples are demonstrated to compare the 2 distributed algorithms with the traditional extended Kalman filter in terms of estimation accuracy and converging rate.

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“…Applications of average consensus include formation control [1], distributed Kriged Kalman filtering [2], distributed merging of feature-based maps [3], and distributed estimation for motion coordination [4]. We study the problem of average consensus over a random graph topology using the polynomial linear protocol with focus on the proportional (P) and proportional-integral (PI) estimators [5].…”

Section: Introductionmentioning

confidence: 99%

Asymptotic mean ergodicity of average consensus estimators

Scoy

Freeman

Lynch

2014

2014 American Control Conference

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Dynamic average consensus estimators suitable for the decentralized computation of global averages of constant or slowly-varying local inputs include the proportional (P) and proportional-integral (PI) estimators. We analyze the convergence properties of these estimators when run on i.i.d. random graphs which are connected and balanced on average, but need not be connected or balanced at each time step. The statistics of the steady-state process are found using the Kronecker product covariance and an ergodic theorem is used to determine whether the steady-state process is mean ergodic. We show that for constant inputs the P estimator is asymptotically mean ergodic only for systems with non-zero forgetting factor which do not have zero steady-state error on average. The PI estimator has both the asymptotic mean ergodicity property and zero steadystate error in expectation for constant inputs independent of initial conditions, proving that the time-averaged output of each agent robustly converges to the correct average.

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Distributed Estimation for Motion Coordination in an Unknown Spatially Varying Flowfield

Cited by 27 publications

References 10 publications

Spherical formation tracking control for second‐order agents with unknown general flowfields and strongly connected topologies

Spherical formation tracking control for second‐order agents with unknown general flowfields and strongly connected topologies

Distributed estimation for spatial rigid motion based on dual quaternions

Asymptotic mean ergodicity of average consensus estimators

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