Distributed Adaptive Control for Nonlinear Heterogeneous Multi-agent Systems with Different Dimensions and Time Delay
DOI:
https://doi.org/10.15837/ijccc.2022.4.4800Keywords:
Multi-agent systems, consensus, neural network, nonlinear, Time-delayAbstract
A distributed neural network adaptive feedback control system is designed for a class of nonlinear multi-agent systems with time delay and nonidentical dimensions. In contrast to previous works on nonlinear heterogeneous multi-agent with the same dimension, particular features are proposed for each agent with different dimensions, and similar parameters are defined, which will be combined parameters of the controller. Second, a novel distributed control based on similarity parameters is proposed using linear matrix inequality (LMI) and Lyapunov stability theory, establishing that all signals in a closed loop system are eventually ultimately bounded. The consistency tracking error steadily decreases to a field with a small number of zeros. Finally, simulated examples with different time delays are utilized to test the effectiveness of the proposed control technique.
References
Seo, J. and Kim, Y. and Kim, S. and Tsourdos, A. (2017). Collision Avoidance Strategies for Unmanned Aerial Vehicles in Formation Flight, IEEE Transactions on Aerospace and Electronic Systems, 53(6), 2718-2734, 2017.
https://doi.org/10.1109/TAES.2017.2714898
Zhang, B. and Shang, W. and Cong, S. and Li, Z. (2021). Coordinated Dynamic Control in the Task Space for Redundantly Actuated Cable-Driven Parallel Robots, IEEE/ASME Transactions on Mechatronics, 26(5), 2396-2407, 2021.
https://doi.org/10.1109/TMECH.2020.3038852
Sieber, D. and Hirche, S. (2019). Human-Guided Multirobot Cooperative Manipulation, IEEE/ASME Transactions on Control Systems Technology, 27(4), 1492-1509, 2019.
https://doi.org/10.1109/TCST.2018.2813323
Sharf, M. and Zelazo, D. (2019). Analysis and Synthesis of MIMO Multi-Agent Systems Using Network Optimization, arXiv e-prints, 64(11), 4512-4524, 2019.
https://doi.org/10.1109/TAC.2019.2908258
Reynolds, C. W. and Flocks, H. (1987). And Schools: A Distributed Behavioral Model ACM SIGGRAPH Computer Graphics, 21(4), 25-34, 1987.
https://doi.org/10.1145/37402.37406
Vicsek, T. and Czirok, A. and Ben-Jacob, E. and Cohen, I. and Sochet, O. (1995). Novel Type of Phase Transition in a System of Self-Driven Particles, Physical Review Letters, 75(6), 1226-1229, 1995.
https://doi.org/10.1103/PhysRevLett.75.1226
Jadbabaie, A. and Jie, L. and Morse, A. S. (2003). Coordination of groups of mobile autonomous agents using nearest neighbor rules, IEEE Transactions on Automatic Control, 48(6), 988-1001, 2003.
https://doi.org/10.1109/TAC.2003.812781
Liu, D. and Wang, D. and Li, H. (2014). Decentralized Stabilization for a Class of Continuous- Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach, IEEE Transactions on Neural Networks and Learning Systems, 25(2), 418-428, 2014.
https://doi.org/10.1109/TNNLS.2013.2280013
Han, X. and Mandal, S. and Pattipati, K. R. and Kleinman, D. L. and Mishra, M. (2017). An Optimization-Based Distributed Planning Algorithm: A Blackboard-Based Collaborative Framework, IEEE Transactions on Systems Man and Cybernetics Systems, 44(6), 673-686, 2017.
https://doi.org/10.1109/TSMC.2013.2276392
Zhang, H. and Feng, T. and Yang, G. H. and Liang, H. (2010). Distributed Cooperative Optimal Control for Multiagent Systems on Directed Graphs: An Inverse Optimal Approach, IEEE Transactions on Cybernetics, 45(7), 1315-1326, 2015.
https://doi.org/10.1109/TCYB.2014.2350511
Lin, J. and Hwang, K. and Wang, Y. (2014). A Simple Scheme for Formation Control Based on Weighted Behavior Learning, IEEE Transactions on Neural Networks and Learning Systems, 25(6), 1033-1044, 2014.
https://doi.org/10.1109/TNNLS.2013.2285123
Loia, V. and Vaccaro, A. (2014). Decentralized Economic Dispatch in Smart Grids by Self- Organizing Dynamic Agents, IEEE Transactions on Systems Man and Cybernetics Systems, 44(4), 397-408, 2014.
https://doi.org/10.1109/TSMC.2013.2258909
Perez, I. J. and Cabrerizo, F. J. and Alonso, S. and Herrera-Viedma, E. (2017). A New Consensus Model for Group Decision Making Problems With Non-Homogeneous Experts, IEEE Transactions on Systems Man and Cybernetics Systems, 44(4), 494-498, 2017.
https://doi.org/10.1109/TSMC.2013.2259155
Owliya, M. and Saadat, M. and Jules, G. G. and Goharian, M. and Anane, R. (2013). Agent-Based Interaction Protocols and Topologies for Manufacturing Task Allocation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(1), 38-52, 2013.
https://doi.org/10.1109/TSMCA.2012.2192263
Duan, Z. andWang, Q. and Feng, Z. and Chen, G. (2008). Estimation of Delay on Synchronization Stability in a Class of Complex Systems with Coupling Delays, Taiwanese Journal of Mathematics, 12(8), 2141-2154, 2008.
https://doi.org/10.11650/twjm/1500405140
Cao, Y. J. and Wu, Q. H. (1996). A note on stability of analog neural networks with time delays, IEEE Transactions on Neural Networks, 7(6), 1533-1535, 1996.
https://doi.org/10.1109/72.548184
Chen, T. (2001). Global convergence of delayed dynamical systems, IEEE Transactions on Neural Networks, 12(6), 1532-1536, 2001.
https://doi.org/10.1109/72.963793
Peng, Z. and Wang, D. and Zhang H. and Sun G. (2014). Distributed Neural Network Control for Adaptive Synchronization of Uncertain Dynamical Multiagent Systems, IEEE Transactions on Neural Networks and Learning Systems, 25(8), 1508-1519, 2014.
https://doi.org/10.1109/TNNLS.2013.2293499
Howard A. (2006). Experiments with a Large Heterogeneous Mobile Robot Team: Exploration, Mapping, Deployment and Detection, International Journal of Robotics Research, 25(5), 431-447, 2006.
https://doi.org/10.1177/0278364906065378
Chung, S. J. and Slotine, J. (2009). Cooperative Robot Control and Concurrent Synchronization of Lagrangian Systems, IEEE Transactions on Robotics, 25(3), 686-700, 2009.
https://doi.org/10.1109/TRO.2009.2014125
Fan, Y. and Xiao, T. and Li, Z. (2020). Distributed Fuzzy Adaptive Control for Heterogeneous Nonlinear Multiagent Systems with Similar Composite Structure, Complexity, https://doi: 10.1155/2020/4081904, 2020.
https://doi.org/10.1155/2020/4081904
Qin, B. and Fan, Y. and Xiao, T. and Li, Z. (2021). Distributed type-2 fuzzy adaptive control for heterogeneous nonlinear multiagent systems, Asian Journal of Control, https://doi:org/10.1002/asjc.2566, 2021.
https://doi.org/10.1002/asjc.2566
Zhao, J. and Hill, D. J. and Tao, L. (2011). Synchronization of Dynamical Networks With Nonidentical Nodes: Criteria and Control, IEEE Transactions on Circuits and Systems I: Regular Papers, 58(3), 584-594, 2011.
https://doi.org/10.1109/TCSI.2010.2072330
Lv, Y. and Li, Z. and Duan, Z. and Feng, G. (2015). Novel Distributed Robust Adaptive Consensus Protocols for Linear Multi-agent Systems with Directed Graphs and External Disturbances, International Journal of Control, 90(2), 137-147, 2015.
https://doi.org/10.1080/00207179.2016.1172259
Tong, S. andWang, T. and Li, Y. (2014). Fuzzy Adaptive Actuator Failure Compensation Control of Uncertain Stochastic Nonlinear Systems With Unmodeled Dynamics, IEEE Transactions on Fuzzy Systems, 22(3), 563-574, 2014.
https://doi.org/10.1109/TFUZZ.2013.2264939
Last, E. (2014). Linear Matrix Inequalities in System and Control Theory, Proceedings of the IEEE, 86(12), 2473-2474, 1994.
Additional Files
Published
Issue
Section
License
Copyright (c) 2022 Bo Qin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.