Stress-matrix-based formation scaling control

Yang, Qiliang; Sun, Zhiyong; Cao, Ming; Fang, Hao; Chen, Jie

doi:10.1016/j.automatica.2018.11.046

Cited by 42 publications

(14 citation statements)

References 30 publications

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“…To achieve a planar formation by a group of mobile robots, many formation control algorithms have been designed, most of which require the measurement of relative positions [15], [30], [31] or aligned bearings [9], [32], or communication [15], [33]. Note that in [15], a gradient-based formation stabilization control law is designed to achieve an infinitesimally angle rigid formation, in which the measurements of relative position and wireless communication of neighbors' angle error information are both needed.…”

Section: Application In Multiagent Planar Formationsmentioning

confidence: 99%

Angle Rigidity and Its Usage to Stabilize Multiagent Formations in 2-D

Chen

Cao

2021

IEEE Trans. Automat. Contr.

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Motivated by the challenging formation stabilization problem for mobile robotic teams wherein no distance or relative position measurements are available but each robot can only measure some of relative angles with respect to its neighbors in its local coordinate frame, we develop the notion of "angle rigidity" for a multipoint framework, named "angularity", consisting of a set of nodes embedded in a Euclidean space, and a set of angle constraints among them. Different from bearings or angles defined in a global frame, the angles we use do not rely on the knowledge of a global frame, and are signed according to the counter-clockwise direction. Here, angle rigidity refers to the property specifying that under proper angle constraints, the angularity can only translate, rotate, or scale as a whole when one or more of its nodes are perturbed locally. We first demonstrate that this angle rigidity property, in sharp comparison to bearing rigidity or other reported rigidity related to angles of frameworks in the literature, is not a global property since an angle rigid angularity may allow flex ambiguity. We then construct necessary and sufficient conditions for infinitesimal angle rigidity by checking the rank of an angularity's rigidity matrix. We develop a combinatorial necessary condition for infinitesimal minimal angle rigidity. Using the developed theories, a formation stabilization algorithm is designed for a robotic team to achieve an angle rigid formation, in which only angle measurements are needed. Simulation examples demonstrate the advantages of the proposed angle-only formation control approach.

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Section: Application In Multiagent Planar Formationsmentioning

confidence: 99%

Angle Rigidity and Its Usage to Stabilize Multiagent Formations in 2-D

Chen

Cao

2021

IEEE Trans. Automat. Contr.

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“…Theorem 1. Under Assumptions 1-4, the tracking control laws for the USV system (1) with deferred asymmetric constraints are designed as (23) and (24). Regardless of the initial conditions, the closed-loop signals are ultimately uniformly bounded (UUB).…”

Section: Tracking Controller With Deferred Asymmetric Full-state Cons...mentioning

confidence: 99%

“…Besides, the uncertainty problem is another important factor worth considering, which can influence the control performance greatly. [17][18][19][20][21][22][23] For model uncertainties and random external disturbances, many effective methods have been developed. Reference 24 investigated the nonstrict tracking control scheme for USV, which employed a regression matrix to address the parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Robust neural network‐based tracking control for unmanned surface vessels under deferred asymmetric constraints

Sun

Qin

et al. 2021

Intl J Robust & Nonlinear

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In this article, the constraints on unmanned surface vessels are imposed after a certain time after the system operation. Based on the shift function, this article proposes an asymmetric barrier Lyapunov function and achieves the control scheme with deferred and asymmetric full-state constraints. In addition, radial basis function neural network and an antiwindup compensator are adopted for the problems of uncertainties and input saturation. The simulation results demonstrate the feasibility of the proposed control strategies.

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“…The formation problem for double-integrator agent networks is resolved in [ 23 ] by using output feedback information. A new formation control protocol by using Stress-matrix was proposed in [ 24 ], in which formations composed of multi-agent systems can be scaled down or enlarged freely. In [ 25 ], the multi-agent network is divided into many sub-networks, and the communication weight between the sub-groups is zero.…”

Section: Introductionmentioning

confidence: 99%

Formation Tracking Control for Multi-Agent Networks with Fixed Time Convergence via Terminal Sliding Mode Control Approach

Chen

et al. 2021

Sensors

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This paper investigates formation tracking control for multi-agent networks with fixed time convergence. The control task is that the follower agents are required to form a prescribed formation within a fixed time and the geometric center of the formation moves in sync with the leader. First, an error system is designed by using the information of adjacent agents and a new control protocol is designed based on the error system and terminal sliding mode control (TSMC). Then, via employing the Lyapunov stability theorem and the fixed time stability theorem, the control task is proved to be possible within a fixed time and the convergence time can be calculated by parameters. Finally, numerical results illustrate the feasibility of the proposed control protocol.

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Stress-matrix-based formation scaling control

Cited by 42 publications

References 30 publications

Angle Rigidity and Its Usage to Stabilize Multiagent Formations in 2-D

Angle Rigidity and Its Usage to Stabilize Multiagent Formations in 2-D

Robust neural network‐based tracking control for unmanned surface vessels under deferred asymmetric constraints

Formation Tracking Control for Multi-Agent Networks with Fixed Time Convergence via Terminal Sliding Mode Control Approach

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