Fully distributed hybrid adaptive learning consensus protocols for a class of non-linearly parameterized multi-agent systems

Abstract The fully distributed hybrid adaptive learning consensus problem for a class of non-linearly parameterized multi-agent systems is investigated in this paper. Under the alignment initial condition and by parameter separation technique, Barbalat-like lemma and a novel Lyapunov–Krasovskii functional, the hybrid adaptive learning consensus protocols with time-varying adaptive control gains and differential-difference learning updating laws are presented, which are fully distributed, and the perfect consensus tracking is guaranteed over a finite time interval. Finally, two simulation examples are given to verify the availability and practicability of theoretical results.

[1]  Timothy W. McLain,et al.  Coordinated target assignment and intercept for unmanned air vehicles , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[2]  Jian-Xin Xu,et al.  A survey on iterative learning control for nonlinear systems , 2011, Int. J. Control.

[3]  Li Ma,et al.  Observer-based adaptive fuzzy tracking control of MIMO switched nonlinear systems preceded by unknown backlash-like hysteresis , 2019, Inf. Sci..

[4]  Jinde Cao,et al.  Neuro-adaptive consensus strategy for a class of nonlinear time-delay multi-agent systems with an unmeasurable high-dimensional leader , 2019 .

[5]  Junmin Li,et al.  Adaptive iterative learning protocol design for nonlinear multi-agent systems with unknown control direction , 2018, J. Frankl. Inst..

[6]  Kevin L. Moore,et al.  Iterative Learning Control: Brief Survey and Categorization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Zhengtao Ding,et al.  Distributed adaptive consensus control of nonlinear output-feedback systems on directed graphs , 2016, Autom..

[8]  Xu Jin,et al.  An adaptive control architecture for leader–follower multiagent systems with stochastic disturbances and sensor and actuator attacks , 2019, Int. J. Control.

[9]  Junmin Li,et al.  Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure , 2018, Int. J. Syst. Sci..

[10]  Frank L. Lewis,et al.  Cooperative Output Regulation of Singular Heterogeneous Multiagent Systems , 2016, IEEE Transactions on Cybernetics.

[11]  Gang Feng,et al.  Distributed Event-Triggered Adaptive Control for Consensus of Linear Multi-Agent Systems with External Disturbances , 2020, IEEE Transactions on Cybernetics.

[12]  Chenguang Yang,et al.  Decentralised adaptive control of a class of hidden leader–follower non-linearly parameterised coupled MASs , 2017 .

[13]  Yuezu Lv,et al.  Distributed adaptive consensus protocols for linear multi-agent systems over directed graphs with relative output information , 2017 .

[14]  Mingxuan Sun,et al.  A Barbalat-Like Lemma with Its Application to Learning Control , 2009, IEEE Transactions on Automatic Control.

[15]  Chengzhi Yuan,et al.  Distributed model reference adaptive containment control of heterogeneous uncertain multi-agent systems. , 2019, ISA transactions.

[16]  Zhongsheng Hou,et al.  Consensus tracking of multi-agent systems with time-delays using adaptive iterative learning control , 2017, 2017 6th Data Driven Control and Learning Systems (DDCLS).

[17]  Mengyin Fu,et al.  Consensus of Multi-Agent Systems With General Linear and Lipschitz Nonlinear Dynamics Using Distributed Adaptive Protocols , 2011, IEEE Transactions on Automatic Control.

[18]  Frank L. Lewis,et al.  Adaptive iterative learning reliable control for a class of non-linearly parameterised systems with unknown state delays and input saturation , 2016 .

[19]  Guang-Hong Yang,et al.  Robust consensus control for a class of multi-agent systems via distributed PID algorithm and weighted edge dynamics , 2018, Appl. Math. Comput..

[20]  Kevin L. Moore,et al.  Robust cooperative learning control for directed networks with nonlinear dynamics , 2017, Autom..

[21]  Kazi Chandrima Rahman,et al.  A Survey on Sensor Network , 2010 .

[22]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[23]  Li Sun,et al.  Fountain-Coding Aided Strategy for Secure Cooperative Transmission in Industrial Wireless Sensor Networks , 2016, IEEE Transactions on Industrial Informatics.

[24]  Guang-Hong Yang,et al.  Adaptive Fault Tolerant Control of Cooperative Heterogeneous Systems With Actuator Faults and Unreliable Interconnections , 2016, IEEE Transactions on Automatic Control.

[25]  Xiangyu Wang,et al.  Composite Backstepping Consensus Algorithms of Leader–Follower Higher-Order Nonlinear Multiagent Systems Subject to Mismatched Disturbances , 2018, IEEE Transactions on Cybernetics.

[26]  Iury V. Bessa,et al.  DSVerifier-Aided Verification Applied to Attitude Control Software in Unmanned Aerial Vehicles , 2018, IEEE Transactions on Reliability.

[27]  Dong Yue,et al.  Output-based event-triggered schemes on leader-following consensus of a class of multi-agent systems with Lipschitz-type dynamics , 2018, Inf. Sci..

[28]  Frank L. Lewis,et al.  Distributed adaptive control for synchronization of unknown nonlinear networked systems , 2010, Autom..

[29]  Nana Yang,et al.  Distributed iterative learning coordination control for leader–follower uncertain non-linear multi-agent systems with input saturation , 2019 .

[30]  Zhongsheng Hou,et al.  Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations , 2016, Int. J. Syst. Sci..

[31]  Xu Jin,et al.  Adaptive iterative learning control for high-order nonlinear multi-agent systems consensus tracking , 2016, Syst. Control. Lett..

[32]  Shaocheng Tong,et al.  Observer-based adaptive fuzzy output constrained control for uncertain nonlinear multi-agent systems , 2018, Inf. Sci..

[33]  Daniel W. C. Ho,et al.  A Unified Approach to Practical Consensus with Quantized Data and Time Delay , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[34]  Jiaxi Chen,et al.  Global Fuzzy Adaptive Consensus Control of Unknown Nonlinear Multiagent Systems , 2020, IEEE Transactions on Fuzzy Systems.

[35]  Ying Wang,et al.  Consensus of second-order delayed nonlinear multi-agent systems via node-based distributed adaptive completely intermittent protocols , 2018, Appl. Math. Comput..

[36]  Tingwen Huang,et al.  Consensus of signed networked multi-agent systems with nonlinear coupling and communication delays , 2019, Appl. Math. Comput..

[37]  Xin Huo,et al.  Event-triggered adaptive fuzzy output feedback control of MIMO switched nonlinear systems with average dwell time , 2020, Appl. Math. Comput..

[38]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[39]  Bing Chen,et al.  Neural adaptive tracking control for a class of high-order non-strict feedback nonlinear multi-agent systems , 2018, Neurocomputing.

[40]  Dong Shen,et al.  Iterative learning control with incomplete information: a survey , 2018, IEEE/CAA Journal of Automatica Sinica.

[41]  Qian Ma,et al.  Bipartite output consensus for heterogeneous linear multi-agent systems with fully distributed protocol , 2019, J. Frankl. Inst..

[42]  Jian-Xin Xu,et al.  Leader–follower synchronisation for networked Lagrangian systems with uncertainties: a learning approach , 2016, Int. J. Syst. Sci..

[43]  E.M. Atkins,et al.  A survey of consensus problems in multi-agent coordination , 2005, Proceedings of the 2005, American Control Conference, 2005..

[44]  Wei Lin,et al.  Adaptive control of nonlinearly parameterized systems: a nonsmooth feedback framework , 2002, IEEE Trans. Autom. Control..

[45]  Li Zhang,et al.  Adaptive iterative learning control for nonlinearly parameterized systems with unknown time-varying delays , 2010 .

[46]  Frank L. Lewis,et al.  Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics , 2012, Autom..