Control adaptable indirecto usando Redes Neuronales Dinmicas

En este trabajo se propone un nuevo tipo de control no-lineal por retroalimentacion para una clase de sis- temas continuos no-lineales de una entrada y una salida de la siguiente forma: Se supone que el sistema no-lineal es desconocido, asi una red neuronal dinamica multicapa es usada para identificarlo. Usando un analisis tipo Lyapunov, una nueva ley de actualizacion estable es presentada, ademas la estabilidad global es probada. Finalmente, se presenta la aplicacion de dicha tecnica al sistema no-lineal TaRA mediante simulaciones.

[1]  Héctor J. Sussmann,et al.  Uniqueness of the weights for minimal feedforward nets with a given input-output map , 1992, Neural Networks.

[2]  Frank L. Lewis,et al.  Neural net robot controller with guaranteed tracking performance , 1995, IEEE Trans. Neural Networks.

[3]  Alexander S. Poznyak,et al.  Indirect adaptive control via parallel dynamic neural networks , 1999 .

[4]  Alexander S. Poznyak,et al.  Nonlinear System Approximation by Neural Networks: Error Stability Analysis , 1995, Intell. Autom. Soft Comput..

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Robert T. Bupp s,et al.  A BENCHMARK PROBLEM FOR NONLINEAR CONTROL DESIGN , 1998 .

[7]  Eduardo D. Sontag,et al.  For neural networks, function determines form , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[8]  Kaining Wang,et al.  Robustness and perturbation analysis of a class of nonlinear systems with applications to neural networks , 1994 .

[9]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[10]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[11]  Manolis A. Christodoulou,et al.  Adaptive control of unknown plants using dynamical neural networks , 1994, IEEE Trans. Syst. Man Cybern..

[12]  Sheng Chen,et al.  Practical identification of NARMAX models using radial basis functions , 1990 .

[13]  J. Willems.  Least squares stationary optimal control and the algebraic Riccati equation , 1971 .

[14]  George A. Rovithakis,et al.  Direct adaptive regulation of unknown nonlinear dynamical systems via dynamic neural networks , 1995, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Dennis S. Bernstein,et al.  A BENCHMARK PROBLEM FOR NONLINEAR CONTROL DESIGN , 1998 .

[16]  Alexander S. Poznyak,et al.  Stability analysis of dynamic neural control , 1998 .

[17]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .

[18]  D. Bernstein,et al.  Global stabilization of the oscillating eccentric rotor , 1994 .

[19]  Zhong-Ping Jiang,et al.  Stabilization and Tracking via Output Feedback for the Nonlinear Benchmark System , 1998, Autom..

[20]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[21]  Johan A. K. Suykens,et al.  Artificial neural networks for modelling and control of non-linear systems , 1995 .

[22]  M. Agarwal.  A systematic classification of neural-network-based control , 1997 .

[23]  Marios M. Polycarpou,et al.  High-order neural network structures for identification of dynamical systems , 1995, IEEE Trans. Neural Networks.

[24]  R. M. Sanner,et al.  Neural Networks for Adaptive Control and Recursive Identification: A Theoretical Framework , 1993 .

[25]  Frank L. Lewis,et al.  Identification of nonlinear dynamical systems using multilayered neural networks , 1996, Autom..