Prescribed performance distance-based formation control of Multi-Agent Systems

doi:10.1016/j.automatica.2020.109086

Automatica

Volume 119, September 2020, 109086

https://doi.org/10.1016/j.automatica.2020.109086 Get rights and content

Abstract

This paper presents a novel control protocol for robust distance-based formation control with prescribed performance in which agents are subjected to unknown external disturbances. Connectivity maintenance and collision avoidance among neighboring agents are also handled by the appropriate design of certain performance bounds that constrain the inter-agent distance errors. As an extension to the proposed scheme, distance-based formation centroid maneuvering is also studied for disturbance-free agents, in which the formation centroid tracks a desired time-varying velocity. The proposed control laws are decentralized, in the sense that each agent employs local relative information regarding its neighbors to calculate its control signal. Therefore, the control scheme is implementable on the agents’ local coordinate frames. Using rigid graph theory, input-to-state stability, and Lyapunov based analysis, the results are established for minimally and infinitesimally rigid formations in 2-D or 3-D space. Furthermore, it is argued that the proposed approach increases formation robustness against shape distortions and can prevent formation convergence to incorrect shapes under the effect of external disturbances, which is likely to happen in conventional distance-based formation control methods. Finally, extensive simulation studies clarify and verify the proposed approach.

Introduction

During the past several years, cooperative control of Multi-Agent Systems (MAS), which deals with achieving a global group behavior that is beyond each individual capabilities through local interactions, has attracted increasing attention due to its broad applications (Dorri, Kanhere, & Jurdak, 2018). Control problems in MAS are mainly classified into consensus, formation, containment, flocking, coverage, and rendezvous (Anderson et al., 2008, Cao et al., 2013, Wang et al., 2016). Particularly, formation control refers to the design of appropriate control protocols for stabilizing agents’ positions with respect to each other so that they set up and maintain a predefined geometrical shape. Based on a recent survey (Oh, Park, & Ahn, 2015), the existing approaches can be categorized into position-, displacement-, and distance-based formation control schemes, depending on the sensing and controlled variables. Among them, distance-based formation control is considered to be an attractive architecture since it imposes less implementation issues compared to other methods. In distance-based formation control, agents measure the relative positions with respect to their neighbors and actively control their inter-agent distances in order to reach a desired predefined shape. This approach enables us to design formation control laws in agents’ local coordinate frames, which neither requires global position measurements (e.g., using GPS) nor pre-alignment of agents’ local coordinate frames (e.g., using a compass) (Meng et al., 2016, Oh et al., 2015). In particular, this formation control approach is advantageous not only due to lower agents’ costs (since they use less complex equipment for sensing and local interactions) but also for operation in GPS-denied environments, where unmanned multi-agent systems are used for search and rescue operations, planetary explorations, indoor navigation, and so on (Ramazani, Selmic, & de Queiroz, 2017).

An introduction to distance-based formation control for both undirected and directed inter-agent sensing graphs is found in Anderson et al. (2008). Some early results in distance-based formation control are also given in Cai and de Queiroz, 2014, Dorfler and Francis, 2010, Krick et al., 2009, Oh and Ahn, 2011. In these works, formation acquisition (convergence to a stationary shape) for single integrator agents with undirected minimally and infinitesimally rigid interaction graphs is considered. In recent years, various modified controllers for undirected distance based formation acquisition were also developed considering event-triggered control (Sun, Liu, Huang, Yu, & Anderson, 2019), quantized distance measurements (Sun, Garcia de Marina, Anderson, & Cao, 2018), exponential convergence (Sun, Mou, Anderson, & Cao, 2016), finite-time convergence (Sun, Mou, Deghat, & Anderson, 2015), optimal control (Babazadeh & Selmic, 2019), source-seeking (Barogh & Werner, 2017), nonlinear dynamics (Cai & de Queiroz, 2015), formation scaling (Yang, Sun, Cao, Fang, & Chen, 2019), formation acquisition with desired orientation (Sun, Park, Anderson, & Ahn, 2017), and a multi layered version of distance-based formation acquisition (Ramazani et al., 2017). A method for dealing with the problem of convergence to incorrect equilibrium points (undesired shapes) of distance-based formation acquisition controllers was recently proposed in Anderson et al., 2017, Liu et al., 2019, introducing an additional control variable (triangular signed areas among agents) for a class of 2-D shapes. Furthermore, the authors in Mou, Belabbas, Morse, Sun, and Anderson (2015) analyzed the practical issue of distance mismatches among neighboring agents for undirected distance-based formations and a solution was proposed in De Marina, Cao, and Jayawardhana (2014).

In addition to formation acquisition control, there have been attempts to solve distance-based formation control problems with moving shapes (such as formation tracking and formation maneuvering). In Deghat, Anderson, and Lin (2015), by combining distance-based formation control with consensus protocols, agents move in a target formation shape with a common constant speed. The results are enhanced taking into account the collision avoidance problem and finite time convergence scheme in Wang and Guo (2018) and Sun et al. (2015), respectively. Recently, in Yang, Cao, de Marina, Fang, and Chen (2018), a weighted centroid formation tracking with distance-based control laws was introduced, where the formation centroid tracks a predefined path. However, this methodology requires agents to sense relative orientations as well. Moreover, Babazadeh and Selmic (2019) developed a distance-based optimal formation tracking using State Dependent Riccati Equation with energy constraints, nonetheless, the design methodology relies on a centralized control approach. A distance-based formation maneuvering controller is proposed in Cai and Queiroz (2015) provided that all agents have direct access to the desired time-varying swarm velocity, hence, it requires pre-alignment of agent’s local coordinate systems. Later, Khaledyan et al., 2019, Mehdifar et al., 2019 utilized distributed velocity estimators in distance-based formation maneuvering for single integrator and unicycle agent models, respectively. In these works, agents estimate the desired group velocity that is only available to a leader in order to relax the requirement of direct access to the desired time-varying group velocity. Nevertheless, these schemes also require inter-agent relative orientation measurements in order to be applicable in arbitrary oriented local coordinate systems. In Rozenheck, Zhao, and Zelazo (2015), a distance-based centroid formation maneuvering with a leader is investigated, where the centroid of the formation tracks a constant (or at best a very slowly varying) desired reference velocity.

The existence of external disturbances that affect the agents dynamics is a significant issue of practical interest for MAS applications. It is noteworthy to mention that in distance-based formation control problems, none of the aforementioned works have taken into account external disturbances. Recently, Bae, Lim, Kang, and Ahn (2018) studied the disturbance attenuation problem with the LMI approach in distance-based formation control. However, its results depend on certain LMI feasibility tests, which are not favorable in practice and may increase complexity in the controller design. Moreover, as a practical problem, collision avoidance among agents has been addressed partially in a few of the above mentioned works, such as Babazadeh and Selmic, 2019, Barogh and Werner, 2017, Wang and Guo, 2018. In addition, none of these works has addressed connectivity maintenance among neighboring agents, which is also critical since agents have limited sensing capabilities in practice. Finally, another crucial issue concerns the transient response of the MAS. Towards this direction, Prescribed Performance Control (PPC) (Bechlioulis & Rovithakis, 2008), proposes a simple and constructive procedure based on which the transient performance of the closed-loop system is predetermined by certain user defined performance bounds. This method has been also applied in MAS control (Bechlioulis et al., 2018, Karayiannidis et al., 2012, Macellari et al., 2017). Recently, PPC has been utilized for formation control problems as well, however, these results are mainly applied to displacement-based formation control methods (Bechlioulis and Kyriakopoulos, 2014, Bechlioulis and Rovithakis, 2016, Wang et al., 2018). A recent paper (Verginis, Nikou, & Dimarogonas, 2019) has also employed PPC for MAS, however, rather than solving a formation control problem, by controlling inter-agent relative orientations and distances it solves a distance- and orientation- based multi-agent coordination problem (that cannot ensure convergence to a specific pre-defined shape) where inter-agent interactions are modeled by undirected tree graphs.

In this paper, we propose a robust distance-based formation acquisition control protocol with guaranteed transient performance, connectivity maintenance and collision avoidance among neighboring agents. The target formation is assumed to be minimally and infinitesimally rigid in two or three dimensional space. User defined performance guarantees on the system’s response are achieved by employing time-varying decreasing performance bounds on the inter-agent distance errors. We prove that the proper choice of performance bounds handles the problems of connectivity maintenance and collision avoidance among neighboring agents as well. More specifically, an error transformation technique is used to convert the original constrained error system into a new equivalent unconstrained one, whose stability only ensures satisfaction of certain time-varying constraints of the inter-agent distance errors. Afterwards, we develop a distance-based formation maneuvering control protocol, based on which the centroid of the formation tracks a desired time-varying velocity. It is assumed that the desired centroid velocity is only available to the leader of the group. The proposed control approach is independent of a global coordinate system and can be applied to arbitrarily oriented local coordinate frames. Furthermore, we prove that the proposed approach naturally reduces potential distortions induced by external disturbances, thus preventing convergence to undesired shapes.

The contributions of this work are summarized as follows:¹

•
To the best of our knowledge, there is no previous work addressing distance-based formation control with guaranteed transient and steady state performance as well as connectivity maintenance and collision avoidance.
•
The performance of the proposed scheme does not depend on the upper bound of the external disturbances.
•
This paper solves the distance-based formation maneuvering problem with time-varying reference velocity for arbitrarily oriented local coordinate frames of agents and without requiring any additional measurements on relative orientations.

Section snippets

Preliminaries on graphs and rigidity theory

Consider an undirected graph with $l$ edges and $n$ vertices, denoted by $G ≜ (V, E)$ where $V = {1, 2, \dots, n}$ is the set of vertices and $E = {(i, j) | i, j \in V, i \neq j}$ is the set of edges. The neighbor set of vertex $i$ is defined as $N_{i} (E) = {j \in V ∣ (i, j) \in E}$ . The incidence matrix $H = {h_{i j}} \in R^{l \times n}$ , relates the edges of $G$ with its vertices. Assuming arbitrary edge orientation, the entries of $H$ are defined as $h_{i j} = + 1$ when the $i$ th edge sinks at node $j$ , $h_{i j} = - 1$ , when the $i$ th edge leaves node $j$ , and $h_{i j} = 0$ , otherwise. For any connected

Problem statement

Consider $n$ interacting agents in an $m$ -dimensional space, with $m \in {2, 3}$ , governed by: ${\dot{q}}_{i} = u_{i} + δ_{i} (t), i = 1, \dots, n$ where $q_{i} \in R^{m}$ is the position, $u_{i} \in R^{m}$ is the velocity control input of agent $i$ with respect to a fixed coordinate frame, and $δ_{i} (t) \in R^{m}$ is an unknown, bounded and piece-wise continuous external disturbance vector. Let the desired formation be defined by a minimally and infinitesimally rigid framework $F^{*} = (G^{*}, q^{*})$ where $G^{*} = (V^{*}, E^{*})$ , $\dim (V^{*}) = n$ , $\dim (E^{*}) = l$ , and $q^{*} = col (q_{i}^{*}) \in R^{m n}$ . Moreover, assume that the

Controller design and stability analysis

The following lemma provides a sufficient condition to establish infinitesimal rigidity of the actual formation $F (t)$ based on the distance error bounds (6b).

Lemma 3

If $F^{*}$ is infinitesimally rigid and $\bar{ϑ}$ is a sufficiently small positive constant satisfying:

$\bar{Ψ} (F, F^{*}) ≜ \sum_{(i, j) \in E^{*}} \max {| {\underset{̲}{e}}_{i j} (0) |, | {\bar{e}}_{i j} (0) |} \leq \bar{ϑ}$ , then securing (6b) guarantees that $F$ is also infinitesimally rigid for all time.

Proof

Using (4), (5), $Ψ (F_{q}, F_{p})$ in Lemma 1 can be represented as: $Ψ (F, F^{*}) = \sum_{(i, j) \in E^{*}} {(‖ {\tilde{q}}_{i j} ‖ - d_{i j})}^{2} = \sum_{(i, j) \in E^{*}} e_{i j}^{2} \leq ϑ$ , for a small

Formation acquisition and robustness to distortions and undesired shapes ²

Consider a group of five agents modeled by (3) in a two dimensional space. Assume that the desired formation is a pentagon defined by a minimally and infinitesimally rigid graph with edge set $E^{*} = {(1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (3, 4), (4, 5)}$ . The desired distances between neighboring agents (desired edges lengths) in the rigid framework are assumed to be $d_{12} = d_{23} = d_{34} = d_{45} = \sqrt{2 (1 - cos (2 π ∕ 5))}$ and $d_{13} = d_{14} = \sqrt{2 (1 + cos (π ∕ 5))}$ . The initial positions of the agents are given by $q_{1} (0) = [- 0.8049, 0.6951]$ , $q_{2} (0) = [- 1.3941,$

Conclusion

In this paper, we proposed a novel decentralized robust distance-based formation control law with guaranteed performance for single integrator agents affected by unknown external disturbances. Moreover, by imposing proper predefined performance bounds in the design, we solved the problems of connectivity maintenance and collision avoidance among neighboring agents. Then, the results were extended to solve distance-based formation maneuvering for nominal single integrator agents, in which the

Farhad Mehdifar was born in Tabriz, Iran, in 1992. He received the B.Sc. and M.Sc. degrees in electrical engineering (control systems) from the University of Tabriz, Tabriz, Iran, in 2015 and 2018, respectively. He is currently pursuing his Ph.D. at UCLouvain, Belgium. His research interests include cooperative control of multi-agent systems, formation control, nonlinear control theory, autonomous robots, networked control, and data driven control/optimization.

References (46)

AndersonB.D. et al.
Formation shape control with distance and area constraints
IFAC Journal of Systems and Control
(2017)
AsimowL. et al.
The rigidity of graphs, ii
Journal of Mathematical Analysis and Applications
(1979)
BaroghS.A. et al.
Cooperative source seeking with distance-based formation control and single-integrator agents
IFAC-PapersOnLine
(2017)
MengZ. et al.
Formation control with mismatched compasses
Automatica
(2016)
OhK.-K. et al.
Formation control of mobile agents based on inter-agent distance dynamics
Automatica
(2011)
OhK.-K. et al.
A survey of multi-agent formation control
Automatica
(2015)
SunZ. et al.
Exponential stability for formation control systems with generalized controllers: A unified approach
Systems & Control Letters
(2016)
SunZ. et al.
Distributed stabilization control of rigid formations with prescribed orientation
Automatica
(2017)
VerginisChristos K et al.
Robust formation control in se (3) for tree-graph structures with prescribed transient and steady state performance
Automatica
(2019)
WangX. et al.
Multi-agent distributed coordination control: Developments and directions via graph viewpoint
Neurocomputing
(2016)

YangQ. et al.

Distributed formation tracking using local coordinate systems

Systems & Control Letters

(2018)

YangQ. et al.

Stress-matrix-based formation scaling control

Automatica

(2019)

AndersonB.D. et al.

Rigid graph control architectures for autonomous formations

IEEE Control Systems

(2008)

BabazadehR. et al.

Distance-based multi-agent formation control with energy constraints using sdre

IEEE Transactions on Aerospace and Electronic Systems

(2019)

BaeY.-B. et al.

Disturbance attenuation in distance-based formation control: A linear matrix inequality approach

BechlioulisCharalampos P et al.

A distributed control and parameter estimation protocol with prescribed performance for homogeneous lagrangian multi-agent systems

Autonomous Robots

(2018)

BechlioulisCharalampos P et al.

Robust model-free formation control with prescribed performance and connectivity maintenance for nonlinear multi-agent systems

53rd IEEE Conference on Decision and Control

(2014)

BechlioulisC.P. et al.

Robust adaptive control of feedback linearizable mimo nonlinear systems with prescribed performance

IEEE Transactions on Automatic Control

(2008)

BechlioulisC.P. et al.

Decentralized robust synchronization of unknown high order nonlinear multi-agent systems with prescribed transient and steady state performance

IEEE Transactions on Automatic Control

(2016)

CaiX. et al.

Rigidity-based stabilization of multi-agent formations

Journal of Dynamic Systems, Measurement, and Control

(2014)

CaiX. et al.

Adaptive rigidity-based formation control for multirobotic vehicles with dynamics

IEEE Transactions on Control Systems Technology

(2015)

CaiX. et al.

Formation maneuvering and target interception for multi-agent systems via rigid graphs

Asian Journal of Control

(2015)

CaoY. et al.

An overview of recent progress in the study of distributed multi-agent coordination

IEEE Transactions on Industrial Informatics

(2013)

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Charalampos P. Bechlioulis was born in Arta, Greece, in 1983. He is currently a postdoctoral researcher in the Control Systems Laboratory at the School of Mechanical Engineering of the National Technical University of Athens. He received a diploma in electrical and computer engineering in 2006 (first in his class), a bachelor of science in mathematics in 2011 (second in his class) and a Ph.D. in electrical and computer engineering in 2011, all from the Aristotle University of Thessaloniki, Thessaloniki, Greece. His research interests include nonlinear control with prescribed performance, system identification, control of robotic vehicles, multi-agent systems and object grasping. He has authored more than 80 papers in scientific journals and conference proceedings and 3 book chapters.

Farzad Hashemzadeh was born in Maku, Iran, in 1981. He received the B.Sc. degree in Biomedical Engineering from the Amirkabir University of Technology, Tehran, Iran, in 2003, the M.Sc. degree in Control Engineering from the University of Tehran, Tehran, in 2006, and the Ph.D. degree in Control Engineering from the University of Tabriz, Tabriz, Iran in 2012. In 2012, he joined the Department of Electrical and Computer Engineering, University of Tabriz and he is currently an Associate professor. His current research interests include teleoperation, network control and multi-agent systems.

Mahdi Baradarannia was born in Tabriz, Iran, in 1982. He received his B.Sc. and M.Sc. degrees in Electrical Engineering from the Faculty of Electrical and Computer Engineering, University of Tabriz, Iran, in 2005 and 2007, respectively, where he also received the Ph.D. degree in Electrical Engineering in 2012. He is now an Associate Professor in the Faculty of Electrical and Computer Engineering at University of Tabriz. His research interests currently involve the analysis and control of nonlinear and optimal systems and their applications in robotics, multiagent and cooperative systems and active structures.

^☆: F. Mehdifar is an FRIA/FNRS fellow and his work is also supported by the concerted research action (ARC ) “RevealFlight”. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Michael M. Zavlanos under the direction of Editor Christos G. Cassandras.

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Prescribed performance distance-based formation control of Multi-Agent Systems☆

Abstract

Introduction

Section snippets

Preliminaries on graphs and rigidity theory

Problem statement

Controller design and stability analysis

Formation acquisition and robustness to distortions and undesired shapes 2

Conclusion

IFAC Journal of Systems and Control

Journal of Mathematical Analysis and Applications

IFAC-PapersOnLine

Automatica

Automatica

Automatica

Systems & Control Letters

Automatica

Automatica

Neurocomputing

Systems & Control Letters

Automatica

Rigid graph control architectures for autonomous formations

IEEE Control Systems

Distance-based multi-agent formation control with energy constraints using sdre

IEEE Transactions on Aerospace and Electronic Systems

Disturbance attenuation in distance-based formation control: A linear matrix inequality approach

A distributed control and parameter estimation protocol with prescribed performance for homogeneous lagrangian multi-agent systems

Autonomous Robots

Robust model-free formation control with prescribed performance and connectivity maintenance for nonlinear multi-agent systems

53rd IEEE Conference on Decision and Control

Robust adaptive control of feedback linearizable mimo nonlinear systems with prescribed performance

IEEE Transactions on Automatic Control

Decentralized robust synchronization of unknown high order nonlinear multi-agent systems with prescribed transient and steady state performance

IEEE Transactions on Automatic Control

Rigidity-based stabilization of multi-agent formations

Journal of Dynamic Systems, Measurement, and Control

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IEEE Transactions on Control Systems Technology

Formation maneuvering and target interception for multi-agent systems via rigid graphs

Asian Journal of Control

An overview of recent progress in the study of distributed multi-agent coordination

IEEE Transactions on Industrial Informatics

Formation acquisition and robustness to distortions and undesired shapes ²