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Control of Flexiblelink Manipulators Using Neural Networks addresses the difficulties that arise in controlling the endpoint of a manipulator that has a significant amount of structural flexibility in its links. The nonminimum 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 closedloop performance. This monograph develops and experimentally evaluates several intelligent (neural network based) control techniques to address the problem of controlling the endpoint of flexiblelink manipulators in the presence of all the aforementioned difficulties. To highlight the main issues, a very flexiblelink 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 testbed. The neural networks are trained and employed as online controllers.
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This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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