Formation Control of Mobile Robots and Unmanned Aerial Vehicles
Investigators: Jag Sarangapani (sarangap@mst.edu, 573-341-6775)
Funding Source: National Science Foundation
Project Description:
In this project, the following objectives will be addressed:
 Development and verification of a mathematical framework for formation control of mobile robots
and unmanned aerial vehicles.
 Development of adaptive neural network controllers for guaranteed performance.
Web link for this project: http://web.mst.edu/~sarangap/
Publications:
David Nodland, H. Zargarzadeh, A. Ghosh, and S. Jagannathan, “Neuro-optimal control of an unmanned
helicopter”, Journal of Defense Modeling and Simulation, in Guest editorial by Greg Hudas, D. Mikulski,
and F. Lewis, vol. 11, no. 1, pp.5-18, January 2014.
1.
T. Dierks, B. Brenner and S. Jagannathan, “Neural network-based optimal control of mobile robot
formation with reduced information exchange”, IEEE Transactions on Control Systems Technology, vol.
21, no. 4, 1407-1415, July 2013.
2.
David Nodland, Hassan Zargarzadeh and S. Jagannathan, “Neural network-based optimal
adaptive output feedback control of a helicopter UAV”, IEEE Transactions on Neural Networks and
Learning Systems, vol. 24, no. 7, pp. 1061-1073, July 2013.
T. Dierks, B. Thumati, and S. Jagannathan, “An online model-based fault accommodation scheme for
nonholonomic mobile robots in formation”, Journal of Defense Modeling and Simulation, in Guest editorial
by Greg Hudas, D. Mikulski, and F. Lewis, vol. 9, no. 1, pp.17-32, January 2012.
3.
T. Dierks, B. Brenner, and S. Jagannathan, “Discrete time optimal control of nonholonomic mobile
robot formations using linearly parameterized neural networks”, International Journal of Robotics and
Automation, vol. 26, no. 1, pp. 76-85, 2011. (invited)