DocumentCode :
2498331
Title :
Near optimal control of mobile robot formations
Author :
Dierks, Travis ; Brenner, Bryan ; Jagannathan, S.
Author_Institution :
Strategic Initiatives, DRS Sustainment Syst., Inc., St. Louis, MO, USA
fYear :
2011
fDate :
11-15 April 2011
Firstpage :
234
Lastpage :
241
Abstract :
In this paper, the infinite horizon optimal tracking control problem is solved online and forward-in-time for leader-follower based formation control of nonholonomic mobile robots. Using the backstepping design approach, the dynamical controller inputs for the robots are approximated from nonlinear optimal control techniques in order to track the control velocities designed to keep the formation. The proposed nonlinear optimal control technique, referred to as adaptive dynamic programming, uses neural networks (NN´s) to solve the optimal formation control problem in discrete-time in the presence of unknown internal dynamics and a known control coefficient matrix. All NN´s are tuned online using novel weight update laws, and the stability of the entire formation is demonstrated using Lyapunov methods. Simulation results are provided to demonstrate the effectiveness of the proposed approach.
Keywords :
Lyapunov methods; dynamic programming; matrix algebra; mobile robots; neurocontrollers; nonlinear control systems; optimal control; position control; stability; velocity control; Lyapunov methods; adaptive dynamic programming; backstepping design approach; control coefficient matrix; formation stability; infinite horizon optimal tracking control; leader-follower based formation control; mobile robot formation; near optimal control; neural networks; nonholonomic mobile robots; nonlinear optimal control technique; velocity control; Approximation methods; Cost function; Feedforward neural networks; Integrated circuits; Optimal control; Robot kinematics; ADP; Formation Control; Multi-Agent System Control; Nonlinear Optimal Control; Optimal Control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2011 IEEE Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-9887-1
Type :
conf
DOI :
10.1109/ADPRL.2011.5967369
Filename :
5967369
Link To Document :
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