基于二分图最大匹配的多机器鱼可控包含控制
|
摘要
考虑水下机器鱼的运动学约束及包含控制中的领导者选择问题,将可控性理论与包含控制相结合,针对有向多机器鱼系统,提出一种基于二分图最大匹配的多机器鱼可控包含控制算法.首先,针对有向多机器鱼网络拓扑结构,利用二分图最大匹配算法求得满足系统可控的驱动节点,即为领导者,其余节点为跟随者;其次,针对2D仿真机器鱼模型设计相应的包含控制协议,从而实现多机器鱼的可控包含控制,且最终跟随者鱼体前端刚体长边方向与领导者保持一致,并应用Lyapunov稳定性理论证明系统的稳定性;最后,基于URWPGSim2D多机器鱼仿真平台进行两组仿真实验,一组随机选取领导者,另一组采用二分图最大匹配算法确定领导者,对比仿真结果表明,所提算法能够有效地实现多机器鱼的可控包含控制.
The kinematic constraints of underwater robotic fish and the leader selection in containment control are considered in this paper. Combining the controllability theory with containment control, for the directed multiple robotic fish system, a controllable containment control algorithm of multiple robotic fish based on bipartite graph maximum matching is proposed. Firstly, aiming at the directed network topology of multiple robotic fish, the maximum matching algorithm of bipartite graph is used to get the driving nodes satisfying the system controllable as leaders, the others are followers. Then, the corresponding containment control protocol is designed for the model of 2 D simulation robotic fish. Thus, the controllable containment control of multiple robotic fish system is achieved and the rigid body long side direction of follower fish is consistent with leaders. The Lyapunov stability theory is used to prove the stability of the system. Finally, two groups of simulation experimet on the URWPGSim2 D multiple robotic fish simulation platform are carried out. One group randomly selects leaders, and the other group uses the bipartite maximum matching algorithm to determine the leaders. Simulation results show that the proposed algorithm can effectively realize the controllable containment control of multiple robotic fish.
The kinematic constraints of underwater robotic fish and the leader selection in containment control are considered in this paper. Combining the controllability theory with containment control, for the directed multiple robotic fish system, a controllable containment control algorithm of multiple robotic fish based on bipartite graph maximum matching is proposed. Firstly, aiming at the directed network topology of multiple robotic fish, the maximum matching algorithm of bipartite graph is used to get the driving nodes satisfying the system controllable as leaders, the others are followers. Then, the corresponding containment control protocol is designed for the model of 2 D simulation robotic fish. Thus, the controllable containment control of multiple robotic fish system is achieved and the rigid body long side direction of follower fish is consistent with leaders. The Lyapunov stability theory is used to prove the stability of the system. Finally, two groups of simulation experimet on the URWPGSim2 D multiple robotic fish simulation platform are carried out. One group randomly selects leaders, and the other group uses the bipartite maximum matching algorithm to determine the leaders. Simulation results show that the proposed algorithm can effectively realize the controllable containment control of multiple robotic fish.
引文
[1] Meng Z, Ren W, Cao Y, et al. Leaderless and leader-following consensus with communication and input delays under a directed network topology[J]. IEEE Trans on Systems, Man, and Cybernetics, Part B, 2011,41(1):75-88.
[2]宋海裕,俞立,胡鸿翔.牵制控制下的多智能体系统群一致性[J].控制理论与应用, 2012, 29(6):765-772.(Song H Y, Yu L, Hu H X. Group consensus in multi-agent systems via pinning control[J]. Control Theory&Applications, 2012, 29(6):765-772.)
[3]陈世明,王培,赖强,等.二阶有向多智能体网络的可控包含控制[J].控制与决策, 2016, 31(4):745-749.(Chen S M, Wang P, Lai Q, et al. Controllable containment control of second order directed multi-agent networks[J].Control and Decision, 2016, 31(4):745-749.)
[4] Chen S M, Wang P, Li H Y, et al. Controllable containment control of multi-agent systems with strongly connected sub-graph[J]. Control Theory&Applications, 2017,34(3):401-407.
[5] Peng Z, Wang D, Wang J. Containment control of networked autonomous underwater vehicles guided by multiple leaders using predictor-based neural DSC approach[C]. The 5th Int Conf on Intelligent Control and Information Processing(ICICIP). Dalian:IEEE, 2014:360-365.
[6] Kan Z, Shea J M, Dixon W E. Leader-follower containment control over directed random graphs[J].Automatica, 2016, 66(4):56-62.
[7] Dong X W, Shi Z Y, Lu G, et al. Formation-containment analysis and design for high-order linear time-invariant swarm systems[J]. Int J of Robust and Nonlinear Control,2015, 25(17):3439-3456.
[8] Dong X W, Li Q D, Ren Z, et al. Formation-containment control for high-order linear time-invariant multi-agent systems with time delays[J]. J of the Franklin Institute,2015, 352(9):3564-3584.
[9] Liu Y Y, Slotine J J, Barabási A L. Controllability of complex networks[J]. Nature, 2011, 473(7346):167-173.
[10] Zhao B, Guan Y, Wang L. Controllability improvement for multi-agent systems:Leader selection and weight adjustment[J]. Int J of Control, 2016, 89(10):2008-2018.
[11] Xu B, Bai J L, Hao Y L, et al. The research status and progress of cooperative navigation for multiple AUVs[J].Acta Automatica Sinica, 2015, 41(3):445-461.
[12]谭民,王硕.机器人技术研究进展[J].自动化学报,2013, 39(7):963-972.(Tan M, Wang S. Research progress on robotics[J]. Acta Automatica Sinica, 2013, 39(7):963-972.)
[13] Jia Y, Wang L. Leader-follower flocking of multiple robotic fish[J]. IEEE/ASME Trans on Mechatronics,2015, 20(3):1372-1383.
[14] Yu J, Wang C, Xie G. Coordination of multiple robotic fish with applications to underwater robot competition[J].IEEE Trans on Industrial Electronics, 2016, 63(2):1280-1288.
[15] Yu J, Yuan J, Wu Z, et al. Data-driven dynamic modeling for a swimming robotic fish[J]. IEEE Trans on Industrial Electronics, 2016, 63(9):5632-5640.
[16] Ren W, Beard R W. Distributed consensus in multi-vehicle cooperative control:Theory and applications[M]. London:Springer-Verlag London,2008:28-38.
[2]宋海裕,俞立,胡鸿翔.牵制控制下的多智能体系统群一致性[J].控制理论与应用, 2012, 29(6):765-772.(Song H Y, Yu L, Hu H X. Group consensus in multi-agent systems via pinning control[J]. Control Theory&Applications, 2012, 29(6):765-772.)
[3]陈世明,王培,赖强,等.二阶有向多智能体网络的可控包含控制[J].控制与决策, 2016, 31(4):745-749.(Chen S M, Wang P, Lai Q, et al. Controllable containment control of second order directed multi-agent networks[J].Control and Decision, 2016, 31(4):745-749.)
[4] Chen S M, Wang P, Li H Y, et al. Controllable containment control of multi-agent systems with strongly connected sub-graph[J]. Control Theory&Applications, 2017,34(3):401-407.
[5] Peng Z, Wang D, Wang J. Containment control of networked autonomous underwater vehicles guided by multiple leaders using predictor-based neural DSC approach[C]. The 5th Int Conf on Intelligent Control and Information Processing(ICICIP). Dalian:IEEE, 2014:360-365.
[6] Kan Z, Shea J M, Dixon W E. Leader-follower containment control over directed random graphs[J].Automatica, 2016, 66(4):56-62.
[7] Dong X W, Shi Z Y, Lu G, et al. Formation-containment analysis and design for high-order linear time-invariant swarm systems[J]. Int J of Robust and Nonlinear Control,2015, 25(17):3439-3456.
[8] Dong X W, Li Q D, Ren Z, et al. Formation-containment control for high-order linear time-invariant multi-agent systems with time delays[J]. J of the Franklin Institute,2015, 352(9):3564-3584.
[9] Liu Y Y, Slotine J J, Barabási A L. Controllability of complex networks[J]. Nature, 2011, 473(7346):167-173.
[10] Zhao B, Guan Y, Wang L. Controllability improvement for multi-agent systems:Leader selection and weight adjustment[J]. Int J of Control, 2016, 89(10):2008-2018.
[11] Xu B, Bai J L, Hao Y L, et al. The research status and progress of cooperative navigation for multiple AUVs[J].Acta Automatica Sinica, 2015, 41(3):445-461.
[12]谭民,王硕.机器人技术研究进展[J].自动化学报,2013, 39(7):963-972.(Tan M, Wang S. Research progress on robotics[J]. Acta Automatica Sinica, 2013, 39(7):963-972.)
[13] Jia Y, Wang L. Leader-follower flocking of multiple robotic fish[J]. IEEE/ASME Trans on Mechatronics,2015, 20(3):1372-1383.
[14] Yu J, Wang C, Xie G. Coordination of multiple robotic fish with applications to underwater robot competition[J].IEEE Trans on Industrial Electronics, 2016, 63(2):1280-1288.
[15] Yu J, Yuan J, Wu Z, et al. Data-driven dynamic modeling for a swimming robotic fish[J]. IEEE Trans on Industrial Electronics, 2016, 63(9):5632-5640.
[16] Ren W, Beard R W. Distributed consensus in multi-vehicle cooperative control:Theory and applications[M]. London:Springer-Verlag London,2008:28-38.