Distributed adaptive flocking of robotic fish system with a leader of bounded unknown input

Jia, Yongnan; Zhang, Weicun

doi:10.1007/s12555-013-0518-6

Cited by 22 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Make N l = k a k1 = 1, k ∈ F denote the set of followers who has one leader neighbor on time interval [t r , t r+1 ). Let the following energy function as the common Lyapunov function [7]. If the value of derivative is less than zero, the energy optimized leader-follower formation algorithm is stable.…”

Section: The Proof Of the Stabilitymentioning

confidence: 99%

See 1 more Smart Citation

Energy Consumption Optimization for the Formation of Multiple Robotic Fishes Using Particle Swarm Optimization

Dong

Zhang

et al. 2018

Energies

View full text Add to dashboard Cite

The traditional leader-follower formation algorithm can realize the formation of multiply robotic fishes, but fails to consider the energy consumption during the formation. In this paper, the energy optimized leader-follower formation algorithm has been investigated to solve this problem. Considering that the acceleration of robotic fish is tightly linked to the motion state and energy consumption, we optimize the corresponding control parameters of the acceleration to reduce energy consumption during the formation via particle swarm algorithm. The whole process has been presented as follows: firstly we realize the formation on the base of the kinematic model with leader-follower formation algorithm; then the energy consumption on the base of dynamical model are derived; finally we seek the optimal control parameters based on the particle swarm optimization (PSO) algorithm. The dynamics simulation of the energy optimization scheme is conducted to verify the functionality of the proposed energy optimized leader-follower formation algorithm via MATLAB. The optimized results demonstrate that the proposed approach, reducing energy consumption during the formation, is superior to the traditional leader-follower formation algorithm and can reduce energy consumption during the formation. The novelty of the work is that we can reduce the energy consumption during the process of formation by considering the energy consumption, which is a gap in the current research field.

show abstract

Section: The Proof Of the Stabilitymentioning

confidence: 99%

“…However, the leader is virtual and few formation articles have contained real leaders. Based on the consensus algorithm [4] and artificial potential field method [6], a model of distributed adaptive formation of multi-agent system has been put forward [7]. The leader is a genuine fish and has bounded unknown input.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Optimization for the Formation of Multiple Robotic Fishes Using Particle Swarm Optimization

Dong

Zhang

et al. 2018

Energies

View full text Add to dashboard Cite

show abstract

“…In one of the early attempts of modeling natural flocks, Reynold [5] introduced three heuristic rules which enabled agents to stay close to nearby flockmates, match their velocities, and avoid collisions. However, Reynold's rules fail to model some important and complex behaviors within flocks such as obstacle avoidance [6,7], terrain adaptation [8,9], and target tracking/goal seeking behavior [10,11]. In this paper, our focus is on the target tracking behavior of groups of dynamic agents.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, our focus is on the target tracking behavior of groups of dynamic agents. Such a target or a common objective is commonly represented by a virtual leader in literature [11][12][13][14], which can be either dynamic or static.…”

Section: Introductionmentioning

confidence: 99%

Community-based informed agents selection for flocking with a virtual leader

Ganganath

Cheng

Wang

et al. 2016

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

It has been studied that a few informed individuals in a group of interacting dynamic agents can influence the majority to follow the position and velocity of a virtual leader. Previously it has been shown that a cluster-based selection of informed agents can drive more agents to follow the virtual leader compared to a random selection. However, a practical question is: How many informed agents to select? In order to address this, here we propose a novel method for selecting informed agents based on community structures in the initial spatial distribution of agents. The number of informed agents are decided based on the strongest community structure. We test and analyze the performance of the proposed method against random and cluster-based selections of informed agents using extensive computer simulations. Results of our study show that community-based selection can be useful in deciding an optimum number of informed agents such that a majority of the group can achieve their common objective.

show abstract

“…Recent advances in sensing, computation and communication have enabled a group of agents, such as robots, to sense and communicate their relative information and perform tasks in a cooperative way [1,2]. These multiagent robotic and sensor systems have a number of advantages over single agent systems, including robustness to failures of individual agents, ease of reconfiguration, and the ability to perform challenging tasks such as environmental monitoring [3][4][5], target tracking [6][7][8][9], source seeking [10,11], cooperative wireless airborne communication [12], that an individual agent would not be capable of performing.…”

Section: Introductionmentioning

confidence: 99%

Gradient-free numerical optimization-based extremum seeking control for multiagent systems

Vweza

Chong

Lee

2015

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

In this article, we develop a model-and gradient-free numerical optimization-based extremum seeking control scheme to solve the problem of formation control and target tracking in multiagent systems. Unlike in conventional gradient-based extremum seeking control, we do not make the strong assumption that the gradient and/or the Hessian of the objective function can be analytically computed from continuously measured system states. Rather, we employ a derivative-free numerical optimization method that directly builds an approximate model of the objective function. After showing the global convergence of the optimization algorithm, we use a regulator to drive the states of the system to the optimizer of the unknown performance function. We demonstrate the performance of the control scheme by simulations where we control three agents that are given a random initial position and are required to maintain an equilateral triangle formation while tracking and localizing a source signal with unknown spatial dynamics.

show abstract

Distributed adaptive flocking of robotic fish system with a leader of bounded unknown input

Cited by 22 publications

References 12 publications

Energy Consumption Optimization for the Formation of Multiple Robotic Fishes Using Particle Swarm Optimization

Energy Consumption Optimization for the Formation of Multiple Robotic Fishes Using Particle Swarm Optimization

Community-based informed agents selection for flocking with a virtual leader

Gradient-free numerical optimization-based extremum seeking control for multiagent systems

Contact Info

Product

Resources

About