An Iterative Learning Control Approach to Multi-Agent Formations

2015 ◽  
Author(s):  
Michael Quann ◽  
Kira Barton
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Stability And Convergence ◽  
Individual Agent ◽  
Convergence Conditions ◽  
Control Approach ◽  
Formation Tracking ◽  
Multi Agent

This paper presents an iterative learning control (ILC) based method for trading off both individual and formation tracking for multiple agents with heterogeneous dynamics. The proposed framework provides precise trajectory tracking for systems involving repetitive, cooperative motion. The frequency domain based controller has the capability of being able to shift weighting between the desire for precise formations amongst the agents and precise trajectory tracking for each individual agent. Stability and convergence conditions are shown and the proposed controller is validated through simulations.

scholarly journals Formation Tracking via Iterative Learning Control for Multiagent Systems with Diverse Communication Time-Delays

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jialu Zhang ◽  
Yong Fang ◽  
Chenlong Li ◽  
Wenbo Zhu
Keyword(s):  
Multiagent Systems ◽  
Iterative Learning Control ◽  
Time Delays ◽  
Learning Control ◽  
Frequency Domain Analysis ◽  
Iterative Learning ◽  
Convergence Conditions ◽  
Formation Tracking ◽  
Communication Time ◽  
Nyquist Stability

In this paper, we consider the formation tracking problem for multiagent systems with diverse communication time-delays by using iterative learning control (ILC) method based on the frequency domain analysis. A first-order ILC law for multiagent systems with diverse communication time-delays is first proposed and its convergence conditions are given by the general Nyquist stability criterion and Gershgorin’s disk theorem. Then, in order for the system to track accurately, a second-order ILC law is presented. The conditions for system tracking with zero error are established. Numerical simulations show that the proposed ILC laws for multiagent systems with diverse communication time-delays are able to achieve effectively formation tracking. And the convergence speed remains the same as the learning control algorithm without communication delay.

scholarly journals An adaptive iterative learning control approach based on disturbance estimation for manipulator system

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141985219
Author(s):  
Keping Liu ◽  
Yuanyuan Chai ◽  
Zhongbo Sun ◽  
Yan Li
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Uncertain Parameters ◽  
External Disturbances ◽  
Adaptive Iterative Learning Control ◽  
Control Approach ◽  
Disturbance Estimation

An adaptive iterative learning control approach based on disturbance estimation has been developed for trajectory tracking of manipulators with uncertain parameters and external disturbances. The external disturbances are estimated by the feedback iterative learning method, whereas the uncertain parameters are compensated by adaptive control. This approach which is based on the disturbance estimation technique provides a rapid convergence of trajectory tracking errors. According to the Lyapunov theory, the sufficient condition of the asymptotic stability has been developed for the 2-degrees of freedom (DOFs) manipulator system. The numerical results show that the adaptive iterative learning control approach based on disturbance estimation is feasible and effective for the 2-DOFs manipulator. A comparison of the adaptive iterative learning control method and the iterative learning control method is completed, which shows that the adaptive iterative learning control method performs a faster convergence of the disturbance to the steady state.

Trajectory Tracking of a One-Link Flexible Arm via Iterative Learning Control

2020 ◽  
Author(s):  
Michele Pierallini ◽  
Franco Angelini ◽  
Riccardo Mengacci ◽  
Alessandro Palleschi ◽  
Antonio Bicchi ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Flexible Arm
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