Control of Flexible-link Manipulators Using Neural Networks

• Κύριοι συγγραφείς: Talebi, H.A (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Patel, R.V (http://id.loc.gov/vocabulary/relators/aut), Khorasani, K. (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: London : Springer London : Imprint: Springer, 2001.
• Έκδοση: 1st ed. 2001.
• Σειρά: Lecture Notes in Control and Information Sciences, 261
• Θέματα: Control engineering.; Robotics.; Mechatronics.; Control, Robotics, Mechatronics.

 Control of Flexible-link Manipulators Using Neural Networks addresses the difficulties that arise in controlling the end-point of a manipulator that has a significant amount of structural flexibility in its links. The non-minimum phase characteristic, coupling effects, nonlinearities, parameter variations and unmodeled dynamics in such a manipulator all contribute to these difficulties. Control strategies that ignore these uncertainties and nonlinearities generally fail to provide satisfactory closed-loop performance. This monograph develops and experimentally evaluates several intelligent (neural network based) control techniques to address the problem of controlling the end-point of flexible-link manipulators in the presence of all the aforementioned difficulties. To highlight the main issues, a very flexible-link manipulator whose hub exhibits a considerable amount of friction is considered for the experimental work. Four different neural network schemes are proposed and implemented on the experimental test-bed. The neural networks are trained and employed as online controllers. 

 Manipulator model -- Output redefinition -- Proposed neural network structures -- Experimental results.