| dc.contributor | P�rez-Cruz, J.H., Centro Universitario de Ciencias Exactas e Ingenier�as, Universidad de Guadalajara Blvd., Marcelino Garc�a Barrag�n No. 1421, C.P. 44430, Guadalajara, Jalisco, Mexico, Secci�n de Estudios de Posgrado e Investigaci�n, ESIME UA-IPN, Av. de las Granjas no. 682, Col. Santa Catarina, C.P. 02250, Mexico City, D.F., Mexico; Chairez, I., Departamento de Bioelectr�nica, UPIBI-IPN, Av. Acueducto s/n, Barrio La Laguna, Col.Ticom�n, C.P. 07340, Mexico City, D.F., Mexico; De Jes�s Rubio, J., Secci�n de Estudios de Posgrado e Investigaci�n, ESIME UA-IPN, Av. de las Granjas no. 682, Col. Santa Catarina, C.P. 02250, Mexico City, D.F., Mexico; Pacheco, J., Secci�n de Estudios de Posgrado e Investigaci�n, ESIME UA-IPN, Av. de las Granjas no. 682, Col. Santa Catarina, C.P. 02250, Mexico City, D.F., Mexico | |
| dc.description.abstract | In this study, a neuro-controller with adaptive deadzone compensation for a class of unknown SISO non-linear systems in a Brunovsky form with uncertain deadzone input is presented. Based on a proper smooth parameterisation of the deadzone, the unknown dynamics is identified by using a continuous time recurrent neural network whose weights are adjusted on-line by stable differential learning laws. On the basis of this neural model so obtained, a feedback linearisation controller is developed in order to follow a bounded reference trajectory specified. By means of Lyapunov analysis, the boundedness of all the closed-loop signals as well as the weights and deadzone parameter estimations is rigorously proven. Besides, the exponential convergence of the actual tracking error to a bounded zone is guaranteed. The effectiveness of this scheme is illustrated by a numerical simulation. � The Institution of Engineering and Technology 2014. | |
