Neural networks have become a well-established methodology as exempli? ed by their applications to identi? cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks, controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o? ers a better suited tool to model and control of nonlinear systems. There exist di? erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ? ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem, nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ? tted for real-time implementations.
Inhaltsverzeichnis
Mathematical Preliminaries. - Discrete-Time Adaptive Neural Backstepping. - Discrete-Time Block Control. - Discrete-Time Neural Observers. - Discrete-Time Output Trajectory Tracking. - Real Time Implementation. - Conclusions and Future Work.
