scholarly journals Iterative learning control for multi-agent systems with impulsive consensus tracking

2021 ◽  
Vol 26 (1) ◽  
pp. 130-150
Author(s):  
Xiaokai Cao ◽  
Michal Fečkan ◽  
Dong Shen ◽  
JinRong Wang
Keyword(s):  
Iterative Learning Control ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Initial State ◽  
Impulsive Input ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Multi Agent ◽  
Piecewise Continuous ◽  
Virtual Leader

In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory.

Fast Finite-Time Consensus Tracking of First-Order Multi-Agent Systems with a Virtual Leader

2014 ◽  
Vol 596 ◽  
pp. 552-559 ◽  
Author(s):  
Qiu Yun Xiao ◽  
Zhi Hai Wu ◽  
Li Peng
Keyword(s):  
Graph Theory ◽  
Finite Time ◽  
Function Method ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
First Order ◽  
Consensus Tracking ◽  
Lyapunov Function Method ◽  
Multi Agent ◽  
Virtual Leader

This paper proposes a novel finite-time consensus tracking protocol for guaranteeing first-order multi-agent systems with a virtual leader to achieve the fast finite-time consensus tracking. The Lyapunov function method, algebra graph theory, homogeneity with dilation and some other techniques are employed to prove that first-order multi-agent systems with a virtual leader applying the proposed protocol can reach the finite-time consensus tracking. Furthermore, theoretical analysis and numerical simulations show that compared with the traditional finite-time consensus tracking protocols, the proposed protocol can accelerate the convergence speed of achieving the finite-time consensus tracking.

scholarly journals Nonlinear Attitude and Formation Estimation Spacecraft And Multi-Agent Systems

2021 ◽  
Author(s):  
Michael Rososhansky
Keyword(s):  
Particle Filter ◽  
Nonlinear Filtering ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Multi Agent ◽  
Filtering Technique ◽  
Estimation And Control ◽  
And Control

This dissertation examines the state and parameter estimation problem of monolithic spacecraft and multi-agent systems in conjunction with the control algorithms. Nonlinear filtering techniques are investigated and applied to the problems of attitude estimation and control of monolithic spacecraft, distributed flltering for attitude estimation and control of satellite formation flying (SFF), and estimation and control of a multi-agent system in consensus tracking with uncertain dynamic model. The main objective is to investigate the performance of nonlinear filtering techniques under fault-free and fault-prone scenarios. In essence, the core of this research has been placed on identifying techniques to improve the efficiency and reduce the variance of estimations in nonlinear filtering. The research is primarily dedicated to the investigation of adaptive unscented Kalman Filter (AUKF) and particle Filter (PF). A nonlinear filtering technique has been proposed for sequential joint estimation of a multi-agent system in consensus tracking with uncertain dynamic model. The new filter is called marginalized unscented particle Filter (MUPF). The proposed filter uses the Rao-Blackwellised principle to couple the particle filtering technique with unscented transform algorithm

scholarly journals Consensus tracking problem for linear fractional multi-agent systems with initial state error

2020 ◽  
Vol 25 (5) ◽  
Author(s):  
Dahui Luo ◽  
JingRong Wang ◽  
Dong Shen
Keyword(s):  
Tracking Problem ◽  
Multi Agent Systems ◽  
Initial State ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Multi Agent ◽  
P Type ◽  
Asymptotical Convergence ◽  
Theoretical Results ◽  
State Error

In this paper, we discuss the consensus tracking problem by introducing two iterative learning control (ILC) protocols (namely, Dα-type and PDα-type) with initial state error for fractional-order homogenous and heterogenous multi-agent systems (MASs), respectively. The initial state of each agent is fixed at the same position away from the desired one for iterations. For both homogenous and heterogenous MASs, the Dα-type ILC rule is first designed and analyzed, and the asymptotical convergence property is carefully derived. Then, an additional P-type component is added to formulate a PDα-type ILC rule, which also guarantees the asymptotical consensus performance. Moreover, it turns out that the PDα-type ILC rule can further adjust the final performance. Two numerical examples are provided to verify the theoretical results.

Sign in / Sign up

Export Citation Format

Share Document