Swarm Coordination Under Conflict and Use of Enhanced Lyapunov Based Controller

2009 ◽  
Author(s):  
Daniel A. Sierra ◽  
Paul McCullough ◽  
Nejat Olgac ◽  
Eldridge Adams
Keyword(s):  
Stability Analysis ◽  
Distributed Control ◽  
Complex Nature ◽  
Control Logic ◽  
Group Pursuit ◽  
Dissipative Control ◽  
Multi Agent ◽  
Two Phases ◽  
The Stability ◽  
Approach Phase

We consider hostile conflicts between two multi-agent swarms. First, we investigate the complex nature of a single pursuer attempting to intercept a single evader (1P-1E), and establish some rudimentary rules of engagement. We elaborate on the stability repercussions of these rules. Second, we extend the modelling and stability analysis between multi-agent swarms of pursuers and evaders. The present document considers only swarms with equal membership strengths for simplicity. This effort is based on a set of suggested momenta deployed on individual agents. Due to the strong nonlinearities, Lyapunov-based stability analysis is used. The control of a group pursuit is divided into two phases: the approach phase during which the two swarms act like individuals in the 1P-1E interaction; and the assigned pursuit phase where each pursuer is assigned to an evader. A dissipative control momentum was suggested in an earlier publication, which caused undesirable control chatter. This study introduces a distributed control logic which ameliorates the chatter problems considerably.

Swarm Coordination Under Conflict and Use of Enhanced Lyapunov Control

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Daniel A. Sierra ◽  
Paul McCullough ◽  
Nejat Olgac ◽  
Eldridge Adams
Keyword(s):  
Stability Analysis ◽  
Time Window ◽  
Single Step ◽  
Complex Nature ◽  
Control Logic ◽  
Wide Range ◽  
New Strategy ◽  
Dissipative Control ◽  
Parametric Effects ◽  
Multi Agent

We consider hostile conflicts between two multi-agent swarms. First, we investigate the complex nature of a single pursuer attempting to intercept a single evader (1P-1E), and establish some rudimentary rules of engagement. The stability repercussions of these rules are investigated using a Lyapunov-based stability analysis. Second, we extend the modeling and stability analysis to interactions between multi-agent swarms of pursuers and evaders. The present document considers only swarms with equal membership strengths for simplicity. This effort is based on a set of suggested momenta deployed on individual agents. The control of group pursuit is divided into two phases: the approach phase during which the two swarms act like individuals in the 1P-1E interaction, and the assigned pursuit phase, where each pursuer follows an assigned evader. A simple, single-step dissipative control strategy, which results in undesirable control chatter, is considered first. A distributed control logic is then introduced, in order to ameliorate the chatter problems. In this new logic, the dissipative control action is spread out over a time window. A wide range of case studies is tested in order to quantify the parametric effects of the new strategy.

Maximizing Walking Step Length for a Near Omni-Directional Hexapod Robot

2004 ◽  
Author(s):  
James P. Schmiedeler ◽  
Nathan J. Bradley ◽  
Brett Kennedy
Keyword(s):  
Stability Analysis ◽  
Step Length ◽  
Static Stability ◽  
Optimization Approach ◽  
Computationally Efficient ◽  
Constant Height ◽  
Planning Algorithm ◽  
Two Phases ◽  
The Stability ◽  
Path Planning Algorithm

A foot path planning algorithm is presented for a robot with six limbs symmetrically located on the faces of its hexagonal body, enabling it to walk at a constant height with an alternating tripod gait. The symmetry results in near omni-directional locomotion capability, so the algorithm is formulated for walking in any direction and at any height. The approach is to determine the maximum length foot path through each limb’s workspace and then modify those foot paths based upon static stability analysis. The stability analysis is conducted in two phases to ensure stability without excessively reducing step length. Compared to an optimization approach, the algorithm yields foot paths within 9.1% of the maximal foot paths for all directions and heights. Unlike the optimization approach, the developed algorithm is computationally efficient enough to be implemented in realtime.

Application of Sliding Mode Control to Swarms Under Conflict

2010 ◽  
Author(s):  
Rudy Cepeda-Gomez ◽  
Nejat Olgac ◽  
Daniel A. Sierra
Keyword(s):  
Sliding Mode Control ◽  
Dynamic Models ◽  
Sliding Mode ◽  
Control Parameters ◽  
Mode Control ◽  
Drag Forces ◽  
Newtonian Dynamic ◽  
Multi Agent ◽  
Two Phases ◽  
The Stability

A robustizing Sliding Mode Control (SMC) strategy is implemented on two competing multi-agent swarms, called pursuers and evaders. Newtonian dynamic models are considered, which include drag forces as well as the inter-agent attraction/repulsion forces. The proposed control achieves the stability and the capture of the evaders by the pursuers despite the uncertainties in the evader behavior. The group pursuit is conceived in two phases: the approach phase during which the two swarms act like two individuals; and the assigned pursuit phase when each pursuer is assigned to an evader. Furthermore, we take into account a turning action for the evaders, which adds to their agility. This property is considered as a part of the uncertainty in the dynamics. The control parameters are separately studied to assess their influences on the pursuit.

scholarly journals Stability analysis method and application of multi-agent systems from the perspective of hybrid systems

2021 ◽  
Vol 54 (9-10) ◽  
pp. 1347-1355
Author(s):  
Zhenhua Yu ◽  
Xiaobo Li ◽  
Emad Abouel Nasr ◽  
Haitham A Mahmoud ◽  
Liang Xu
Keyword(s):  
Stability Analysis ◽  
Hybrid Systems ◽  
Invariant Sets ◽  
Continuous Variables ◽  
Analysis Method ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Complex Adaptive ◽  
Multi Agent ◽  
The Stability

Many multi-agent systems (MASs) can be regarded as hybrid systems that contain continuous variables and discrete events exhibiting both continuous and discrete behavior. An MAS can accomplish complex tasks through communication, coordination, and cooperation among different agents. The complex, adaptive and dynamic characteristics of MASs can affect their stability that is critical for MAS performance. In order to analyze the stability of MASs, we propose a stability analysis method based on invariant sets and Lyapunov’s stability theory. As a typical MAS, an unmanned ground vehicle formation is used to evaluate the proposed method. We design discrete modes and control polices for the MAS composed of unmanned ground vehicles to guarantee that the agents can cooperate with each other to reliably achieve a final assignment. Meanwhile, the stability analysis is given according to the definition of MAS stability. Simulation results illustrate the feasibility and effectiveness of the proposed method.

A Lyapunov Treatment of Swarm Coordination Under Conflict

2009 ◽  
Author(s):  
Paul McCullough ◽  
Mark Bacon ◽  
Nejat Olgac ◽  
Daniel A. Sierra
Keyword(s):  
Stability Analysis ◽  
Control Strategy ◽  
Interaction Force ◽  
Equal Mass ◽  
Local Vectors ◽  
Two Phases ◽  
Integrator Model ◽  
Force Profiles ◽  
Double Integrator ◽  
Approach Phase

This document considers hostile conflicts between two swarms, called pursuers and evaders, based on a double integrator model. In order to convert the marginally stable dynamics into stable-capture behavior, we introduce a control strategy based on the relative positions and velocities of opposing swarm members. To evaluate its effectiveness, a Lyapunov-based stability analysis is performed. For simplicity, this document considers swarms with equal membership strengths and equal mass only. The modeling is based on a set of suggested interaction force profiles, which are functions of local vectors. The group pursuit is conceived in two phases: the approach phase during which the two swarms act like two individuals; and the assigned pursuit phase where each pursuer is assigned to an evader.

Distributed Adaptive Control for Multi-Agent Systems with Pseudo Higher Order Neural Net

2013 ◽  
pp. 194-213 ◽  
Author(s):  
Abhijit Das ◽  
Frank Lewis
Keyword(s):  
Nonlinear Systems ◽  
Distributed Control ◽  
Higher Order ◽  
Neural Net ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Complex Nonlinear Systems ◽  
The Stability ◽  
Minimum Interaction

The idea of using multi-agent systems is becoming more popular every day. It not only saves time and resources but also eliminates much of the human workload. These ideas are especially effective in the combat zone, where multiple unmanned aerial vehicles can achieve simultaneous objectives or targets. The evolution of distributed control started with a simple integrator systems, and then different control methodologies have been adopted for more and more complex nonlinear systems. In addition, from a practical standpoint, the dynamics of the agents involved in networked control architecture might not be identical. Therefore, an ideal distributed control should accommodate multiple agents that are nonlinear systems associated with unknown dynamics. In this chapter, a distributed control methodology is presented where nonidentical nonlinear agents communicate among themselves following directed graph topology. In addition, the nonlinear dynamics are considered unknown. While the pinning control strategy has been adopted to distribute the input command among the agents, a Pseudo Higher Order Neural Net (PHONN)-based identification strategy is introduced for identifying the unknown dynamics. These two strategies are combined beautifully so that the stability of the system is assured even with minimum interaction among the agents. A detailed stability analysis is presented based on the Lyapunov theory, and a simulation study is performed to verify the theoretical claims.

Stability Analysis of the Nanjung Water Diversion Twin Tunnels based on Convergence Measurement

2019 ◽  
Vol 1 (1) ◽  
pp. 49-60
Author(s):  
Simon Heru Prassetyo ◽  
Ganda Marihot Simangunsong ◽  
Ridho Kresna Wattimena ◽  
Made Astawa Rai ◽  
Irwandy Arif ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Convergence Rate ◽  
Critical Strain ◽  
Convergence Rates ◽  
Strain Analysis ◽  
Water Diversion ◽  
Tunnel Face ◽  
Tunnel Stability ◽  
The Stability ◽  
Twin Tunnels

This paper focuses on the stability analysis of the Nanjung Water Diversion Twin Tunnels using convergence measurement. The Nanjung Tunnel is horseshoe-shaped in cross-section, 10.2 m x 9.2 m in dimension, and 230 m in length. The location of the tunnel is in Curug Jompong, Margaasih Subdistrict, Bandung. Convergence monitoring was done for 144 days between February 18 and July 11, 2019. The results of the convergence measurement were recorded and plotted into the curves of convergence vs. day and convergence vs. distance from tunnel face. From these plots, the continuity of the convergence and the convergence rate in the tunnel roof and wall were then analyzed. The convergence rates from each tunnel were also compared to empirical values to determine the level of tunnel stability. In general, the trend of convergence rate shows that the Nanjung Tunnel is stable without any indication of instability. Although there was a spike in the convergence rate at several STA in the measured span, that spike was not replicated by the convergence rate in the other measured spans and it was not continuous. The stability of the Nanjung Tunnel is also confirmed from the critical strain analysis, in which most of the STA measured have strain magnitudes located below the critical strain line and are less than 1%.

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