期刊文献+

多机器人系统的鲁棒一致性算法

A Robust Consensus Algorithm for Multirobot Systems
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摘要 针对部分信息已知且带有干扰的多机器人系统,设计一种新的鲁棒一致性算法,解决多机器人系统输出一致性问题.首先,给出轮式移动机器人系统的运动学模型,根据全局控制与局部控制策略相结合的思想,建立基于多智能体一致行为的数学描述;其次,提出一种合理的鲁棒性能指标,利用H∞鲁棒控制及微分对策方法,设计多机器人系统鲁棒一致性算法.仿真实验验证了上述算法不仅使多个机器人自主达到速度一致,并有效抑制了干扰. For a multirobot system subjected to exogenous disturbance and with partial informatlon given, a robust consensus algorithm is developed to solve the output consensus problems. The kinematic model of a differential wheeled mobile robot is presented first. According to the idea of combining global control with local control, the consensus based on a multi-agent system is mathematically described. Then, a reasonable robust performance index is proposed, and utilizing H∞ robust control and differential game theory, a robust consensus algorithm is designed. An example is simulated to demonstrate that the proposed method can guarantee the velocity consensus autonomously for each robot and restrain disturbance.
出处 《东北大学学报(自然科学版)》 EI CAS CSCD 北大核心 2014年第8期1065-1068,共4页 Journal of Northeastern University(Natural Science)
基金 国家自然科学基金资助项目(61374137) 流程工业综合自动化国家重点实验室基础科研业务费资助项目(2013ZCX02-03)
关键词 机器人 H∞鲁棒控制 微分对策 多智能体 一致性 robot H∞ robust control differential game multi-agent consensus
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参考文献9

  • 1Arai T, Pagello E, Parker L E. Guest editorial: advances in multi-robot systems[ J]. IEEE Transactions on Robotics and Automation ,2002,18 (5) :655 - 661.
  • 2张云洲,吴成东,薛定宇,王斐.自主移动机器人嵌入式控制系统研究[J].东北大学学报(自然科学版),2008,29(1):29-32. 被引量:13
  • 3段勇,崔宝侠,徐心和.多智能体强化学习及其在足球机器人角色分配中的应用[J].控制理论与应用,2009,26(4):371-376. 被引量:26
  • 4Chang Y H, Chang C W, Chen C L, et al. Fuzzy sliding-mode formation control for multirobot systems: design and implementation[ J ]. IEEE Transactions on Systems,Man ,and Cybernetics-Part B: Cybernetics, 2012,42 ( 2 ) :444 - 457.
  • 5Olfati-Saber R, Murray R M. Consensus problems in networks of agents with switching topology and time-delays [J].1EEE Transactions on Automatic Control, 2004,49 ( 9 ) : 1520 - 1533.
  • 6Semsar K E, Khorasanti K. Optimal consensus algorithm for cooperative team of agents subject to partial information [J ]. Automatica ,2008,44 ( 11 ) :2766 - 2777. 9.
  • 7Siljak D D. Large-scale dynamic systems: stability & structure[M]. Mineola: Dover Publications, 1978.
  • 8Zhou K, Doyle J C, Glower K. Robust and optimal control [ M]. Upper Saddle River: Prentice Hall, 1996.
  • 9Basar T, Olsder G J. Dynamic noncooperative game theory [ M]. 2nd ed. Philadelphia:Society for Industrial and Applied Mathematics, 199.

二级参考文献19

  • 1赵红,李雅菊,宋涛.基于贝叶斯网络的工程项目风险管理[J].沈阳工业大学学报(社会科学版),2008,1(3):239-244. 被引量:25
  • 2方正,杨华,胡益民,徐心和.嵌入式智能机器人平台研究[J].机器人,2006,28(1):54-58. 被引量:30
  • 3李晓毅,徐兆棣.增量式贝叶斯分类的原理和算法[J].沈阳工业大学学报,2006,28(4):422-425. 被引量:7
  • 4KIM J H, VADAKEPAT E Multi-agent systems: a survey from the robot-soccer perspective[J]. International Journal of Intelligent Automation and Soft Computing, 2000, 6(1) : 3 - 17.
  • 5STONE P, VELOSO M. Multiagent systems: a survey from a machine learning perspective[J]. Autonomous Robots, 2000, 8(3) : 345 - 383.
  • 6ERFU Y, DONGBING G. Multiagent reinforcement learning for multirobot systems: a survey[R]. Technical Report CSM-404, Department of Computer Science, University of Essex, 2004.
  • 7LITrMAN M L. Markov games as a framework for multiagent learning[C] // Proceeding of the 11th International Conference on Machine Learning. San Francisco: IEEE, 1994, 157 - 163.
  • 8HU J L, WELLMAN M E Multiagent reinforcement learning: theoretical framework and an algorithm[C]//Proceeding of the 15th International Conference of Machine Learning. San Francisco: IEEE, 1998, 115 - 122.
  • 9SUTI'ON R S, BATRO A G. Reinforcement Learning: An Introduction[M]. Cambridge, Massachusetts: MIT, 1998.
  • 10DOMINGOS P, PAZZANI M. On the optimality of the simple bayesian classifier under zero-one loss[J]. Machine Learning, 1997, 29(2/3): 103 -130.

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