Mathematical model with sensor and actuator for a transelevator

In this paper, the structural, sensor, and actuator mathematical models of a transelevator are presented; after, the mathematical model with sensor and actuator of the transelevator is obtained by using the combination of the above mentioned mathematical models. The proposed mathematical model is validated comparing the simulation results against the experimental results. Finally, the stability analysis of the aforementioned model is studied.

[1]  Erol Sahin,et al.  Steering self-organized robot flocks through externally guided individuals , 2010, Neural Computing and Applications.

[2]  Edwin Lughofer,et al.  On-line elimination of local redundancies in evolving fuzzy systems , 2011, Evol. Syst..

[3]  Floriberto Ortiz-Rodríguez,et al.  A method for online pattern recognition of abnormal eye movements , 2011, Neural Computing and Applications.

[4]  José de Jesús Rubio,et al.  Modeling of the relative humidity via functional networks and control of the temperature via classic controls for a bird incubator , 2011, Neural Computing and Applications.

[5]  Daniel F. Leite,et al.  Evolving fuzzy granular modeling from nonstationary fuzzy data streams , 2012, Evol. Syst..

[6]  Enrique García,et al.  Trajectory planning and collisions detector for robotic arms , 2011, Neural Computing and Applications.

[7]  M. Spong,et al.  Robot Modeling and Control , 2005 .

[8]  Tsung-Hsien Yang,et al.  Combining GRN modeling and demonstration-based programming for robot control , 2009, Neural Computing and Applications.

[9]  J. de Jesus Rubio,et al.  Comparison of four mathematical models for braking of a motorcycle , 2011, IEEE Latin America Transactions.

[10]  Manuel Graña,et al.  Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera , 2011, Neural Computing and Applications.

[11]  Chih-Hui Chiu,et al.  Self-tuning output recurrent cerebellar model articulation controller for a wheeled inverted pendulum control , 2010, Neural Computing and Applications.

[12]  Yoshio Inoue,et al.  Identification of a golf swing robot using soft computing approach , 2011, Neural Computing and Applications.

[13]  Hicham Chaoui,et al.  Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines , 2011, Neural Computing and Applications.

[14]  Alma Y. Alanis,et al.  System Identification Using Multilayer Differential Neural Networks: A New Result , 2012, J. Appl. Math..

[15]  Jacob Beal,et al.  Composable continuous-space programs for robotic swarms , 2010, Neural Computing and Applications.

[16]  Walmir M. Caminhas,et al.  Fuzzy evolving linear regression trees , 2011, Evol. Syst..

[17]  U. Filobello-Niño,et al.  A general solution for troesch's problem , 2012 .

[18]  Fuchun Sun,et al.  A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system , 2010, Neural Computing and Applications.

[19]  J. Humberto Pérez-Cruz,et al.  Robust Adaptive Neurocontrol of SISO Nonlinear Systems Preceded by Unknown Deadzone , 2012 .

[20]  Frank L. Lewis,et al.  Control of Robot Manipulators , 1993 .

[21]  David Johan Christensen,et al.  Anatomy-based organization of morphology and control in self-reconfigurable modular robots , 2010, Neural Computing and Applications.

[22]  Su-Ling Lee,et al.  The Convergent Behavior for Parametric Generalized Vector Equilibrium Problems , 2012, J. Appl. Math..