Adjacent-Agent Dynamic Linearization-Based Iterative Learning Formation Control

The dynamical relationship of the multiple agents’ behavior in a networked system is explored and utilized to enhance the control performance of the multiagent formation in this paper. An adjacent-agent dynamic linearization is first presented for nonlinear and nonaffine multiagent systems (MASs) and a virtual linear difference model is built between two adjacent agents communicating with each other. Considering causality, the agents are assigned as parent and child, respectively. Communication is from parent to child. Taking the advantage of the repetitive characteristics of a large class of MASs, an adjacent-agent dynamic linearization-based iterative learning formation control (ADL-ILFC) is proposed for the child agent using 3-D control knowledge from iterations, time instants, and the parent agent. The ADL-ILFC is a data-driven method and does not depend on a first-principle physical model but the virtual linear difference model. The validity of the proposed approach is demonstrated through rigorous analysis and extensive simulations.

[1]  Z. Hou,et al.  Formation control for a class of nonlinear multiagent systems using model‐free adaptive iterative learning , 2018 .

[2]  Ziyang Meng,et al.  Adaptive collision-free formation control for under-actuated spacecraft , 2018, Aerospace Science and Technology.

[3]  Deyuan Meng,et al.  Robust Tracking of Nonrepetitive Learning Control Systems With Iteration-Dependent References , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  Carlos Canudas-de-Wit,et al.  Cooperative Control Design for Time-Varying Formations of Multi-Agent Systems , 2014, IEEE Transactions on Automatic Control.

[5]  Kevin L. Moore,et al.  Robust Iterative Learning Control for Nonrepetitive Uncertain Systems , 2017, IEEE Transactions on Automatic Control.

[6]  Daqi Zhu,et al.  An Adaptive SOM Neural Network Method for Distributed Formation Control of a Group of AUVs , 2018, IEEE Transactions on Industrial Electronics.

[7]  Ziyang Meng,et al.  Uniform convergence for signed networks under directed switching topologies , 2018, Autom..

[8]  Daero Lee,et al.  Asymptotic Tracking Control for Spacecraft Formation Flying with Decentralized Collision Avoidance , 2015 .

[9]  Weidong Zhang,et al.  Adaptive Second-Order Fast Nonsingular Terminal Sliding Mode Tracking Control for Fully Actuated Autonomous Underwater Vehicles , 2019, IEEE Journal of Oceanic Engineering.

[10]  Deyuan Meng,et al.  Dynamic Distributed Control for Networks With Cooperative–Antagonistic Interactions , 2018, IEEE Transactions on Automatic Control.

[11]  Mingjun Du,et al.  Edge Convergence Problems on Signed Networks , 2019, IEEE Transactions on Cybernetics.

[12]  Hongjing Liang,et al.  Event-Triggered Adaptive Tracking Control for Multiagent Systems With Unknown Disturbances , 2020, IEEE Transactions on Cybernetics.

[13]  Miao Yu,et al.  Robust Adaptive Iterative Learning Control for Discrete-Time Nonlinear Systems With Time-Iteration-Varying Parameters , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Shangtai Jin,et al.  A unified data-driven design framework of optimality-based generalized iterative learning control , 2015, Comput. Chem. Eng..

[15]  Zhongsheng Hou,et al.  Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition , 2008, Autom..

[16]  Simon Haykin,et al.  Adaptive filter theory (2nd ed.) , 1991 .

[17]  Toru Namerikawa,et al.  Consensus-based cooperative formation control with collision avoidance for a multi-UAV system , 2014, 2014 American Control Conference.

[18]  Youmin Zhang,et al.  Tracking control of spacecraft formation flying with collision avoidance , 2015 .

[19]  Ye Yan,et al.  Dual-quaternion based distributed coordination control of six-DOF spacecraft formation with collision avoidance , 2017 .

[20]  Deyuan Meng,et al.  Convergence Conditions for Solving Robust Iterative Learning Control Problems Under Nonrepetitive Model Uncertainties , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Yongsun Kim,et al.  Leader-following formation control of quadcopters with heading synchronization , 2015 .

[22]  Yisheng Zhong,et al.  Time-Varying Formation Control for Unmanned Aerial Vehicles: Theories and Applications , 2015, IEEE Transactions on Control Systems Technology.

[23]  Shangtai Jin,et al.  Computationally Efficient Data-Driven Higher Order Optimal Iterative Learning Control , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Peter Corke,et al.  Nonlinear Model Predictive Formation Control for Quadcopters* , 2015, SyRoCo.

[25]  孙明轩,et al.  Learning identification of a class of stochastic time-varying systems with colored noise , 2012 .

[26]  Junmin Li,et al.  Adaptive iterative learning control for coordination of second‐order multi‐agent systems , 2014 .

[27]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[28]  Shangtai Jin,et al.  Computationally‐Light Non‐Lifted Data‐Driven Norm‐Optimal Iterative Learning Control , 2018 .

[29]  Sauro Longhi,et al.  Nonlinear Decentralized Model Predictive Control Strategy for a Formation of Unmanned Aerial Vehicles , 2012 .

[30]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .

[31]  Xu Jin,et al.  Fault tolerant finite-time leader-follower formation control for autonomous surface vessels with LOS range and angle constraints , 2016, Autom..

[32]  Chao Yang,et al.  Time-varying formation tracking of multiple manipulators via distributed finite-time control , 2016, Neurocomputing.

[33]  Zhongsheng Hou,et al.  Model Free Adaptive Control: Theory and Applications , 2013 .

[34]  Haibo He,et al.  Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics , 2018, IEEE Transactions on Cybernetics.

[35]  Heinz Unbehauen,et al.  Adaptive position control of electrohydraulic servo systems using ANN , 2000 .

[36]  Zhang Ren,et al.  Time-Varying Formation Tracking for Second-Order Multi-Agent Systems Subjected to Switching Topologies With Application to Quadrotor Formation Flying , 2017, IEEE Transactions on Industrial Electronics.

[37]  Weidong Zhang,et al.  Double-Loop Integral Terminal Sliding Mode Tracking Control for UUVs With Adaptive Dynamic Compensation of Uncertainties and Disturbances , 2019, IEEE Journal of Oceanic Engineering.

[38]  Kevin L. Moore,et al.  Trajectory‐keeping in satellite formation flying via robust periodic learning control , 2010 .