Adaptive Formation Control Using Artificial Potentials for Euler-Lagrange Agents

Abstract In this paper, we present a formation control strategy for a group of agents modeled as Euler-Lagrange systems. The formation is achieved by means of a desired kinematic model generated by artificial potentials. The system uncertainties are compensated by binary adaptive control which combines the good transient properties and robustness of Sliding Mode Control with the desirable steady-state properties of parameter adaptive systems. Furthermore, an important advantage with respect to sliding mode control is that the proposed controller generates a continuous signal so that control chattering is avoided. A simplified version of the controller is also proposed, which does not require the knowledge of the velocities of the neighboring vehicles.

[1]  K.M. Passino,et al.  Stability analysis of social foraging swarms , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Randal W. Beard,et al.  A decentralized approach to formation maneuvers , 2003, IEEE Trans. Robotics Autom..

[3]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[4]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[5]  Liu Hsu,et al.  Adaptive control with discontinuous σ-factor and saturation for improved robustness , 1987 .

[6]  Zhong-Ping Jiang,et al.  Small-gain theorem for ISS systems and applications , 1994, Math. Control. Signals Syst..

[7]  Vijay Kumar,et al.  Controlling formations of multiple mobile robots , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[8]  V. Kapila,et al.  Adaptive learning control for spacecraft formation flying , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[9]  Naomi Ehrich Leonard,et al.  Virtual leaders, artificial potentials and coordinated control of groups , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[10]  YangQuan Chen,et al.  Formation control: a review and a new consideration , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  K. Khorasani,et al.  Adaptive formation control of UAVs in the presence of unknown vortex forces and leader commands , 2006, 2006 American Control Conference.

[12]  R. Ordonez,et al.  Swarm Tracking Using Artificial Potentials and Sliding Mode Control , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[13]  L. Hsu,et al.  Analysis and design of I/O based variable structure adaptive control , 1994, IEEE Trans. Autom. Control..

[14]  Petros A. Ioannou,et al.  Robust redesign of adaptive control , 1984 .

[15]  Fred Y. Hadaegh,et al.  Adaptive Control of Formation Flying Spacecraft for Interferometry , 1998 .

[16]  Kar-Han Tan,et al.  Virtual structures for high-precision cooperative mobile robotic control , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[17]  Veysel Gazi,et al.  Swarm aggregations using artificial potentials and sliding mode control , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[18]  Guangming Xie,et al.  Leader-following formation control of multiple mobile vehicles , 2007 .