"synopsis" may belong to another edition of this title.
The objective of this work is to present recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, in order to guarantee its properties; in addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the book includes a chapter presenting experimental results related to their application to an electric three phase induction motor, which show the applicability of such designs. The proposed schemes could be employed for different applications beyond the ones presented in this book.
The book presents solutions for the output trajectory tracking problem of unknown nonlinear systems based on four schemes. For the first one, a direct design method is considered: the well known backstepping method, under the assumption of complete state measurement; the second one considers an indirect method, solved with the block control and the sliding mode techniques, under the same assumption. For the third scheme, the backstepping technique is reconsidering including a neural observer, and finally the block control and the sliding mode techniques are used again too, with a neural observer. All the proposed schemes are developed in discrete-time. For both mentioned control methods as well as for the neural observer, the on-line training of the respective neural networks is performed by Kalman Filtering.
"About this title" may belong to another edition of this title.
Book Description Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Seller Inventory # ABEOCT23-193952
Book Description Condition: New. Brand New Original US Edition.We Ship to PO BOX Address also. EXPEDITED shipping option also available for faster delivery.This item may ship from the US or other locations in India depending on your location and availability. Seller Inventory # ABTR-218835
Book Description Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed. This item may ship from the US or our Overseas warehouse depending on your location and stock availability. We Ship to PO BOX Location also. Seller Inventory # ABRR-218835
Book Description Hardcover. Condition: new. Seller Inventory # 9783540782889
Book Description Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Seller Inventory # ABEOCT23-256513
Book Description Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Seller Inventory # ria9783540782889_lsuk
Book Description Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 120 pp. Englisch. Seller Inventory # 9783540782889
Book Description Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputsNeural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and con. Seller Inventory # 4900896
Book Description Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783540782889