Distributed consensus for nonlinear multi-agent systems with two-time-scales: A hybrid reinforcement learning consensus algorithm*

被引:3
作者
Peng, Chuanjun [1 ,2 ]
Xia, Jianwei [3 ]
Wang, Jing [1 ,2 ]
Shen, Hao [1 ,2 ]
机构
[1] Anhui Univ Technol, AnHui Prov Key Lab Special Heavy Load Robot, Maanshan 243032, Peoples R China
[2] Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan 243032, Peoples R China
[3] Liaocheng Univ, Sch Math Sci, Liaocheng 252059, Peoples R China
基金
中国国家自然科学基金;
关键词
Fuzzy model method; Multi-agent systems; Two-time-scales; Hybrid reinforcement learning; ADAPTIVE OPTIMAL-CONTROL; TRACKING CONTROL; LINEAR-SYSTEMS; OPTIMIZATION;
D O I
10.1016/j.ins.2023.119091
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The optimal consensus problem for nonlinear two-time-scales multi-agent systems with completely unknown system dynamics is investigated in this paper. First, the original system is linearly represented based on the Takagi-Sugeno fuzzy model. Then, the optimal consensus problem for multi-agent systems is transformed into solving the game algebraic Riccati equation associated with agents and their neighbors. And individual agent dynamics studied in this paper are replaced with local error dynamics. Moreover, an offline hybrid iteration algorithm with rapid convergence speed and no initial stable control policy is presented for multi-agent systems. Meanwhile, to avoid the utilization of the knowledge of system matrices, an online hybrid reinforcement learning algorithm that only uses the state and control input data of each agent and its neighbors is given to generate the distributed optimal control policy. The convergence of proposed algorithms is also discussed. Finally, the applicability of the presented method is illustrated by an example.
引用
收藏
页数:15
相关论文
共 42 条
[1]   Distributed Consensus Algorithms for a Class of High-Order Multi-Agent Systems on Directed Graphs [J].
Abdessameud, Abdelkader ;
Tayebi, Abdelhamid .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2018, 63 (10) :3464-3470
[2]   Stochastic approximation or nonexpansive maps:: Application to Q-learning algorithms [J].
Abounadi, J ;
Bertsekas, DP ;
Borkar, V .
SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 2002, 41 (01) :1-22
[3]   Fuzzy logic-based generalized decision theory with imperfect information [J].
Aliev, Rafik ;
Pedrycz, Witold ;
Fazlollahi, Bijan ;
Huseynov, O. H. ;
Alizadeh, A. V. ;
Guirimov, B. G. .
INFORMATION SCIENCES, 2012, 189 :18-42
[4]   Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization [J].
Aliev, Rafik A. ;
Pedrycz, Witold ;
Guirimov, Babek G. ;
Aliev, Rashad R. ;
Ilhan, Umit ;
Babagil, Mustafa ;
Mammadli, Sadik .
INFORMATION SCIENCES, 2011, 181 (09) :1591-1608
[5]   Control design with guaranteed cost for synchronization in networks of linear singularly perturbed systems [J].
Ben Rejeb, Jihene ;
Morarescu, Irinel-Constantin ;
Daafouz, Jamal .
AUTOMATICA, 2018, 91 :89-97
[6]   Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design [J].
Bian, Tao ;
Jiang, Zhong-Ping .
AUTOMATICA, 2016, 71 :348-360
[7]   Distributed evolutionary optimization, in Manifold: Rosenbrock's function case study [J].
Bouvry, P ;
Arbab, F ;
Seredynski, F .
INFORMATION SCIENCES, 2000, 122 (2-4) :141-159
[8]   Finite-Time Consensus for Linear Multi-Agent Systems Using Data-Driven Terminal ILC [J].
Bu, Xuhui ;
Zhu, Panpan ;
Hou, Zhongsheng ;
Liang, Jiaqi .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2020, 67 (10) :2029-2033
[9]   Data-Driven Terminal Iterative Learning Consensus for Nonlinear Multiagent Systems With Output Saturation [J].
Bu, Xuhui ;
Liang, Jiaqi ;
Hou, Zhongsheng ;
Chi, Ronghu .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (05) :1963-1973
[10]   Off-policy learning for adaptive optimal output synchronization of heterogeneous multi-agent systems [J].
Chen, Ci ;
Lewis, Frank L. ;
Xie, Kan ;
Xie, Shengli ;
Liu, Yilu .
AUTOMATICA, 2020, 119