A heterogeneous control gain approach to achieve a desired collective centroid by a formation of agents
01 Jan 2017-Vol. 2017, pp 326
TL;DR: This paper proposes a heterogeneous gains based controller design methodology to stabilize a particular type of collective motion in a multi-agent system where the heading angles of the agents are in balanced formation and derives feedback control laws that operate with heterogeneous control gains.
Abstract: This paper proposes a heterogeneous gains based controller design methodology to stabilize a particular type of collective motion in a multi-agent system where the heading angles of the agents are in balanced formation. Balancing refers to the situation in which the movement of agents causes the position of their centroid to become stationary. Our interest, in this paper, is to achieve balanced formation about a desired location of the centroid while allowing the agents to move either along straight line paths or around individual circular orbits. For this purpose, we derive feedback control laws that operate with heterogeneous control gains, and are more practical compared to the homogeneous gains based controls existing in the literature. We also show that if the heterogeneous control gains are zero for almost half of the agents of the group, it is possible to achieve balanced formation at an additional advantage of reduced computational complexity of the proposed control law. Simulations are given to illustrate the theoretical findings.
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TL;DR: This paper analyses collective motion of multi-vehicle systems in balanced or splay formation when the vehicles are equipped with heterogeneous controller gains and proposes strategies to achieve such balanced and splay formations about a desired centroid location while allowing the vehicles to move either along straight line paths or on individual circular orbits.
Abstract: This paper analyses collective motion of multi-vehicle systems in balanced or splay formation when the vehicles are equipped with heterogeneous controller gains. Balancing refers to a situation in which the positional centroid of the vehicles is stationary. The splay formation is a special case of balancing in which the vehicles are spatially distributed with equal angular separation between them. The paper proposes strategies to achieve such balanced and splay formations about a desired centroid location while allowing the vehicles to move either along straight line paths or on individual circular orbits. Feedback control laws that can tolerate heterogeneity in the controller gains, which may be caused by imperfect implementation, are derived and analyzed. It is shown that drastic failures leading to controller gains becoming zero for almost half of the vehicles in the group can be tolerated and balanced formation can still be achieved. On the other hand, splay formation can still be achieved if the controller gain is zero for at most one vehicle. Simulation examples are given to illustrate the theoretical findings.
2 citations
References
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TL;DR: A distinctive feature of this work is to address consensus problems for networks with directed information flow by establishing a direct connection between the algebraic connectivity of the network and the performance of a linear consensus protocol.
Abstract: In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results.
11,658 citations
"A heterogeneous control gain approa..." refers background in this paper
...A contrary notion of balancing is synchronization, which refers to the situation when all the agents of a group have a common velocity direction, and is widely studied in the literature [3]−[7]....
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TL;DR: In this article, the authors review 25 years of research on the Kuramoto model, highlighting the false turns as well as the successes, but mainly following the trail leading from Kuramoto's work to Crawford's recent contributions.
2,795 citations
"A heterogeneous control gain approa..." refers background or methods in this paper
...In [21] and [22], the equally spaced circular formation of multiple robots is stabilized by using a Kuramoto-like model [23]....
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...In literature [23], pθ is also known as the phase order parameter, and its absolute value |pθ|, which satisfies 0 ≤ |pθ| ≤ 1, tells about the phase coherence of the heading angles θ....
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08 Jun 2005
TL;DR: A survey of consensus problems in multi-agent cooperative control with the goal of promoting research in this area is provided in this paper, where theoretical results regarding consensus seeking under both time-invariant and dynamically changing information exchange topologies are summarized.
Abstract: As a distributed solution to multi-agent coordination, consensus or agreement problems have been studied extensively in the literature. This paper provides a survey of consensus problems in multi-agent cooperative control with the goal of promoting research in this area. Theoretical results regarding consensus seeking under both time-invariant and dynamically changing information exchange topologies are summarized. Applications of consensus protocols to multiagent coordination are investigated. Future research directions and open problems are also proposed.
1,382 citations
TL;DR: The results of the paper provide a low-order parametric family of stabilizable collectives that offer a set of primitives for the design of higher-level tasks at the group level.
Abstract: This paper proposes a design methodology to stabilize isolated relative equilibria in a model of all-to-all coupled identical particles moving in the plane at unit speed. Isolated relative equilibria correspond to either parallel motion of all particles with fixed relative spacing or to circular motion of all particles with fixed relative phases. The stabilizing feedbacks derive from Lyapunov functions that prove exponential stability and suggest almost global convergence properties. The results of the paper provide a low-order parametric family of stabilizable collectives that offer a set of primitives for the design of higher-level tasks at the group level
528 citations
"A heterogeneous control gain approa..." refers background in this paper
...The present work is inspired by the problem addressed in [8], where, gradient based steering control laws are derived to stabilize synchronized and balanced formations in a group of agents moving either along the straight lines or around individual circular orbits or around a common circular orbit....
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...The phase arrangement θ is balanced if the phase order parameter (2) equals zero, that is, pθ = 0 [8]....
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...In the situation where the agents are in balanced formation around a common circle, it is shown in [8] that the collective centroid coincides with the center of the common circle, and...
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Book•
09 Mar 2004
TL;DR: In this paper, the authors present a mathematical model for the trajectory of a single-stage ballistic missile, which is based on the D'Alembert's principle of transformation properties of Vectors.
Abstract: Contents 1 Introduction References 2 The Generalized Missile Equations of Motion 2.1 Coordinate Systems 2.1.1 Transformation Properties of Vectors 2.1.2 Linear Vector Functions 2.1.3 Tensors 2.1.4 Coordinate Transformations 2.2 Rigid-Body Equations of Motion 2.3 D'Alembert's Principle 2.4 Lagrange's Equations for Rotating Coordinate Systems References 3 Aerodynamic Forces and Coefficients 3.1 Aerodynamic Forces Relative to the Wind Axis System 3.2 Aerodynamic Moment Representation 3.2.1 Airframe Characteristics and Criteria 3.3 System Design and Missile Mathematical Model 3.3.1 System Design 3.3.2 The Missile Mathematical Model 3.4 The Missile Guidance System Model 3.4.1 The Missile Seeker Subsystem 3.4.2 Missile Noise Inputs 3.4.3 Radar Target Tracking Signal 3.4.4 Infrared Tracking Systems 3.5 Autopilots 3.5.1 Control Surfaces and Actuators 3.6 English Bias References 4 Tactical Missile Guidance Laws 4.1 Introduction 4.2 Tactical Guidance Intercept Techniques 4.2.1 Homing Guidance 4.2.2 Command and Other Types of Guidance 4.3 Missile Equations of Motion 4.4 Derivation of the Fundamental Guidance Equations 4.5 Proportional Navigation 4.6 Augmented Proportional Navigation 4.7 Three-Dimensional Proportional Navigation 4.8 Application of Optimal Control of Linear Feedback Systems with Quadratic Performance Criteria in Missile Guidance 4.8.1 Introduction 4.8.2 Optimal Filtering 4.8.3 Optimal Control of Linear Feedback Systems with Quadratic Performance Criteria 4.8.4 Optimal Control for Intercept Guidance 4.9 End Game References 5 Weapon Delivery Systems 5.1 Introduction 5.2 Definitions and Acronyms Used in Weapon Delivery 5.2.1 Definitions 5.2.2 Acronyms 5.3 Weapon Delivery Requirements 5.3.1 Tactics and Maneuvers 5.3.2 Aircraft Sensors 5.4 The Navigation/Weapon Delivery System 5.4.1 The Fire Control Computer 5.5 Factors In.uencing Weapon Delivery Accuracy 5.5.1 Error Sensitivities 5.5.2 Aircraft Delivery Modes 5.6 Unguided Weapons 5.6.1 Types of Weapon Delivery 5.6.2 Unguided Free-Fall Weapon Delivery 5.6.3 Release Point Computation for Unguided Bombs 5.7 The Bombing Problem 5.7.1 Conversion of Ground Plane Miss Distance into Aiming Plane Miss Distance 5.7.2 Multiple Impacts 5.7.3 Relationship Among REP, DEP, and CEP 5.8 Equations of Motion 5.9 Covariance Analysis 5.10 Three-Degree-of-Freedom Trajectory Equations and Error Analysis 5.10.1 Error Analysis 5.11 Guided Weapons 5.12 Integrated Flight Control in Weapon Delivery 5.12.1 Situational Awareness/Situation Assessment (SA/SA) 5.12.2 Weapon Delivery Targeting Systems 5.13 Air-to-Ground Attack Component 5.14 Bomb Steering 5.15 Earth Curvature 5.16 Missile Launch Envelope 5.17 Mathematical Considerations Pertaining to the Accuracy of Weapon Delivery Computations References 6 Strategic Missiles 6.1 Introduction 6.2 The Two-Body Problem 6.3 Lambert's Theorem 6.4 First-Order Motion of a Ballistic Missile 6.4.1 Application of the Newtonian Inverse-Square Field Solution to Ballistic Missile Flight 6.4.2 The Spherical Hit Equation 6.4.3 Ballistic Error Coef.cients 6.4.4 Effect of the Rotation of the Earth 6.5 The Correlated Velocity and Velocity-to-Be-Gained Concepts 6.5.1 Correlated Velocity 6.5.2 Velocity-to-Be-Gained 6.5.3 The Missile Control System 6.5.4 Control During the Atmospheric Phase 6.5.5 Guidance Techniques 6.6 Derivation of the Force Equation for Ballistic Missiles 6.6.1 Equations of Motion 6.6.2 Missile Dynamics 6.7 Atmospheric Reentry 6.8 Missile Flight Model 6.9 Ballistic Missile Intercept 6.9.1 Introduction 6.9.2 Missile Tracking Equations of Motion References 7 Cruise Missiles 7.1 Introduction 7.2 System Description<7.2.1 System Functional Operation and Requirements 7.2.2 Missile Navigation System Description 7.3 Cruise Missile Navigation System Error Analysis 7.3.1 Navigation Coordinate System 7.4 Terrain Contour Matching (TERCOM) 7.4.1 Introduction 7.4.2 De.nitions 7.4.3 The Terrain-Contour Matching (TERCOM) Concept 7.4.4 Data Correlation Techniques 7.4.5 Terrain Roughness Characteristics 7.4.6 TERCOM System Error Sources 7.4.7 TERCOM Position Updating 7.5 The NAVSTAR/GPS Navigation System 7.5.1 GPS/INS Integration References A Fundamental Constants B Glossary of Terms C List of Acronyms D The Standard Atmospheric Model References E Missile Classi.cation F Past and Present Tactical/Strategic Missile Systems F.1 Historical Background F.2 Unpowered Precision-Guided Munitions (PGM) References G Properties of Conics G.1 Preliminaries G.2 General Conic Trajectories References H Radar Frequency Bands I Selected Conversion Factors Index
502 citations
"A heterogeneous control gain approa..." refers methods in this paper
...In accordance with the guidance literature [26], [27], VD is the velocity component of the collective centroid along the line-of-sight (LOS: the line joining the current and the desired centroid), and VθD is the velocity component normal to LOS....
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