Title :
Stable adaptive control with recurrent networks
Author :
Kulawski, G.J. ; Brdys, M.A.
Author_Institution :
Sch. of Electron. & Electr. Eng., Univ. of Birmingham, Birmingham, UK
Abstract :
An adaptive control technique for nonlinear plants with immeasurable state is presented. It is based on a recurrent neural network employed as a dynamical model of the plant. Using this dynamical model, a feedback linearizing control is computed and applied to the plant. Parameters of the model are updated on line to allow for partially unknown and time varying plant. Stability of the scheme is shown theoretically and its performance is illustrated in simulations.
Keywords :
adaptive control; feedback; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; stability; time-varying systems; dynamical model; feedback linearizing control; nonlinear plants; recurrent neural network; stability; stable adaptive control technique; time varying plant; unmeasurable state; Adaptation models; Computational modeling; Convergence; Lyapunov methods; Neural networks; Stability analysis; Trajectory; adaptive control; neural nets; nonlinear control;
Conference_Titel :
Control Conference (ECC), 1997 European
Conference_Location :
Brussels
Print_ISBN :
978-3-9524269-0-6
