A robust H∞ control design for swarm formation control of multi-agent systems: A decentralized adaptive fuzzy approach

In this paper, a decentralized adaptive control scheme for multi-agent formation control is proposed. This control method is based on artificial potential functions integrated with adaptive fuzzy H∞ technique. We consider fully actuated mobile agents with partially unknown models, where an adaptive fuzzy logic system is used to approximate the unknown system dynamics. The H∞ control theory is used to attenuate the adaptive fuzzy approximation error to a prescribed level. Therefore the agents motion is forced to obey the dynamics defined by the simple inter-agent artificial potential functions. Stability proof is given using Lyapunov functions, which shows the robust behavior of controller with respect to disturbances and system uncertainties. Finally, simulation results are demonstrated for a multi-agent formation problem of a group of six agents, illustrating the effective attenuation of fuzzy logic approximation error.

[1]  Yupu Yang,et al.  Leader-following consensus problem with a varying-velocity leader and time-varying delays , 2009 .

[2]  P. Shahmaleki,et al.  Real time experimental study of truck backer upper problem with fuzzy controller , 2008, 2008 World Automation Congress.

[3]  K. Watanabe,et al.  A design of tiny basin test-bed for AUV multi agent , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[4]  Chien Chern Cheah,et al.  Region-based shape control for a swarm of robots , 2009, Autom..

[5]  Karsten Berns,et al.  Development of complex robotic systems using the behavior-based control architecture iB2C , 2010, Robotics Auton. Syst..

[6]  Kimon P. Valavanis,et al.  Swarm Formation Control Utilizing Elliptical Surfaces and Limiting Functions , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  P. Khargonekar,et al.  State-space solutions to standard H/sub 2/ and H/sub infinity / control problems , 1989 .

[8]  Bor-Sen Chen,et al.  H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach , 1996, IEEE Trans. Fuzzy Syst..

[9]  Indra Narayan Kar,et al.  Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots , 2006, IEEE Transactions on Control Systems Technology.

[10]  Dimos V. Dimarogonas,et al.  Connectedness Preserving Distributed Swarm Aggregation for Multiple Kinematic Robots , 2008, IEEE Transactions on Robotics.

[11]  Bor-Sen Chen,et al.  A nonlinear H∞ control design in robotic systems under parameter perturbation and external disturbance , 1994 .

[12]  Kouhei Ohnishi,et al.  Autonomous decentralized control for formation of multiple mobile robots considering ability of robot , 2004, IEEE Transactions on Industrial Electronics.

[13]  A. Tsourdos,et al.  Contaminant Cloud Boundary Monitoring Using Network of UAV Sensors , 2008, IEEE Sensors Journal.

[14]  Michael Defoort,et al.  Sliding-Mode Formation Control for Cooperative Autonomous Mobile Robots , 2008, IEEE Transactions on Industrial Electronics.

[15]  George J. Pappas,et al.  Flocking in Fixed and Switching Networks , 2007, IEEE Transactions on Automatic Control.

[16]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[17]  P. Khargonekar,et al.  STATESPACE SOLUTIONS TO STANDARD 2 H AND H? CONTROL PROBLEMS , 1989 .

[18]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[19]  Hongyan Wang,et al.  Social potential fields: A distributed behavioral control for autonomous robots , 1995, Robotics Auton. Syst..

[20]  Veysel Gazi,et al.  Swarm aggregations using artificial potentials and sliding-mode control , 2005, IEEE Transactions on Robotics.

[21]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1998 .

[22]  Naomi Ehrich Leonard,et al.  Stabilization of Planar Collective Motion: All-to-All Communication , 2007, IEEE Transactions on Automatic Control.