Synchronized tracking control of multi-agent system with limited information

In this paper, synchronized tracking control is considered for multiple agents with unknown system dynamics, while the desired trajectory is only available to portion of the team members. Using the weighted average of the neighbors' outputs, adaptive neural network (NN) tracking control is designed for each agent. Rigid mathematical proof was provided for the proposed algorithm based on the Lyapunov analysis. It is shown that, under the proposed NN control, the output tracking error of each agent converges to an adjustable neighborhood of the origin. Simulations of synchronized altitude tracking of multiple unmanned helicopters are provided to demonstrate the effectiveness of the approaches presented.

[1]  Shuzhi Sam Ge,et al.  Decentralized adaptive control of a class of discrete-time multi-agent systems for hidden leader following problem , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[3]  Shuzhi Sam Ge,et al.  Multi-Robot Formations based on the Queue-Formation Scheme with Limited Communications , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[4]  Ella M. Atkins,et al.  Distributed multi‐vehicle coordinated control via local information exchange , 2007 .

[5]  Keng Peng Tee,et al.  Adaptive Neural Network Control for Helicopters in Vertical Flight , 2008, IEEE Transactions on Control Systems Technology.

[6]  Wei Ren.  On Consensus Algorithms for Double-Integrator Dynamics , 2008, IEEE Trans. Autom. Control..

[7]  Yoo Sang Choo,et al.  Leader-follower formation control of underactuated autonomous underwater vehicles , 2010 .

[8]  Kevin L. Moore,et al.  High-Order and Model Reference Consensus Algorithms in Cooperative Control of MultiVehicle Systems , 2007 .

[9]  W. Marsden.  I and J , 2012 .

[10]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[11]  Shuzhi Sam Ge,et al.  Queues and artificial potential trenches for multirobot formations , 2005, IEEE Transactions on Robotics.

[12]  Jin Zhang,et al.  Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback , 2003, IEEE Trans. Neural Networks.

[13]  P. Wang,et al.  Coordination and control of multiple microspacecraft moving in formation , 1996 .

[14]  Shuzhi Sam Ge,et al.  An ISS-modular approach for adaptive neural control of pure-feedback systems , 2006, Autom..

[15]  Zhihua Qu,et al.  Nonlinear autopilot control design for a 2-DOF helicopter model , 1997 .

[16]  Yang Shi,et al.  Output Feedback Stabilization of Networked Control Systems With Random Delays Modeled by Markov Chains , 2009, IEEE Transactions on Automatic Control.

[17]  Beibei Ren,et al.  Synchronized altitude tracking control of multiple unmanned helicopters , 2010, Proceedings of the 2010 American Control Conference.

[18]  Wei Ren,et al.  Multi-vehicle consensus with a time-varying reference state , 2007, Syst. Control. Lett..

[19]  Shuzhi Sam Ge,et al.  Agent formations in 3D spaces with communication limitations using an adaptive Q-structure , 2010, Robotics Auton. Syst..

[20]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[21]  S. Behtash.  Robust output tracking for non-linear systems , 1990 .

[22]  Daizhan Cheng,et al.  Leader-following consensus of multi-agent systems under fixed and switching topologies , 2010, Syst. Control. Lett..

[23]  Richard M. Murray,et al.  INFORMATION FLOW AND COOPERATIVE CONTROL OF VEHICLE FORMATIONS , 2002 .

[24]  Shuzhi Sam Ge,et al.  Multirobot Formations Based on the Queue-Formation Scheme With Limited Communication , 2007, IEEE Transactions on Robotics.

[25]  F. J. Narcowich,et al.  Persistency of Excitation in Identification Using Radial Basis Function Approximants , 1995 .

[26]  Hong-Bin Ma.  Decentralized adaptive synchronization of a stochastic discrete-time multiagent dynamic model , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.