Premium
Neural network‐based finite horizon optimal adaptive consensus control of mobile robot formations
Author(s) -
Guzey H. M.,
Xu Hao,
Sarangapani Jagannathan
Publication year - 2015
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2222
Subject(s) - control theory (sociology) , bellman equation , optimal control , mobile robot , lyapunov function , identifier , controller (irrigation) , artificial neural network , mathematical optimization , function (biology) , computer science , robot , horizon , mathematics , control (management) , artificial intelligence , physics , nonlinear system , quantum mechanics , evolutionary biology , agronomy , biology , programming language , geometry
Summary In this paper, a novel NN‐based optimal adaptive consensus‐based formation control scheme over finite horizon is presented for networked mobile robots or agents in the presence of uncertain robot/agent dynamics. The uncertain robot formation dynamics are approximated online by using an NN‐based identifier and a suitable weight tuning law. In addition, a novel time‐varying value function is derived by using the augmented error vector, which consists of the regulation and consensus‐based formation errors of each robot. By using the value function approximation and the identified dynamics, the near optimal control input over finite horizon is derived. This finite horizon optimal control leads to a time‐varying value function, which becomes the solution of the Hamilton–Jacobi–Bellman equation, and control input is approximated by a second NN with time‐varying activation function. A novel weight update law for the NN value function is developed to tune the value function, satisfy the terminal constraint, and relax an initial admissible controller requirement. The Lyapunov stability method is utilized to demonstrate the consensus of the overall formation. Finally, simulation results are given to verify theoretical claims. Copyright © 2015 John Wiley & Sons, Ltd.