Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities
The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural netw...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/951983 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560274331140096 |
|---|---|
| author | J. Humberto Pérez-Cruz José de Jesús Rubio Rodrigo Encinas Ricardo Balcazar |
| author_facet | J. Humberto Pérez-Cruz José de Jesús Rubio Rodrigo Encinas Ricardo Balcazar |
| author_sort | J. Humberto Pérez-Cruz |
| collection | DOAJ |
| description | The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed. |
| format | Article |
| id | doaj-art-b66a5817bb874263b548c1e93eeb9ffb |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-b66a5817bb874263b548c1e93eeb9ffb2025-02-03T01:28:04ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/951983951983Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone NonlinearitiesJ. Humberto Pérez-Cruz0José de Jesús Rubio1Rodrigo Encinas2Ricardo Balcazar3Sección de Estudios de Posgrado e Investigación, ESIME UA-IPN, Avenida de las Granjas, No. 682, Colonia Santa Catarina, México, DF 02250, MexicoSección de Estudios de Posgrado e Investigación, ESIME UA-IPN, Avenida de las Granjas, No. 682, Colonia Santa Catarina, México, DF 02250, MexicoSección de Estudios de Posgrado e Investigación, ESIME UA-IPN, Avenida de las Granjas, No. 682, Colonia Santa Catarina, México, DF 02250, MexicoSección de Estudios de Posgrado e Investigación, ESIME UA-IPN, Avenida de las Granjas, No. 682, Colonia Santa Catarina, México, DF 02250, MexicoThe trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.http://dx.doi.org/10.1155/2014/951983 |
| spellingShingle | J. Humberto Pérez-Cruz José de Jesús Rubio Rodrigo Encinas Ricardo Balcazar Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities The Scientific World Journal |
| title | Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities |
| title_full | Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities |
| title_fullStr | Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities |
| title_full_unstemmed | Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities |
| title_short | Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities |
| title_sort | singularity free neural control for the exponential trajectory tracking in multiple input uncertain systems with unknown deadzone nonlinearities |
| url | http://dx.doi.org/10.1155/2014/951983 |
| work_keys_str_mv | AT jhumbertoperezcruz singularityfreeneuralcontrolfortheexponentialtrajectorytrackinginmultipleinputuncertainsystemswithunknowndeadzonenonlinearities AT josedejesusrubio singularityfreeneuralcontrolfortheexponentialtrajectorytrackinginmultipleinputuncertainsystemswithunknowndeadzonenonlinearities AT rodrigoencinas singularityfreeneuralcontrolfortheexponentialtrajectorytrackinginmultipleinputuncertainsystemswithunknowndeadzonenonlinearities AT ricardobalcazar singularityfreeneuralcontrolfortheexponentialtrajectorytrackinginmultipleinputuncertainsystemswithunknowndeadzonenonlinearities |