Download Control of Flexible-link Manipulators Using Neural Networks by H.A. Talebi PDF

By H.A. Talebi

Keep watch over of Flexible-link Manipulators utilizing Neural Networks addresses the problems that come up in controlling the end-point of a manipulator that has an important volume of structural flexibility in its hyperlinks. The non-minimum section attribute, coupling results, nonlinearities, parameter adaptations and unmodeled dynamics in the sort of manipulator all give a contribution to those problems. regulate thoughts that forget about those uncertainties and nonlinearities more often than not fail to supply passable closed-loop functionality. This monograph develops and experimentally evaluates numerous clever (neural community dependent) regulate recommendations to handle the matter of controlling the end-point of flexible-link manipulators within the presence of the entire aforementioned problems. to spotlight the most matters, a really flexible-link manipulator whose hub shows a large amount of friction is taken into account for the experimental paintings. 4 diverse neural community schemes are proposed and applied at the experimental test-bed. The neural networks are expert and hired as on-line controllers.

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Both schemes assume some a priori knowledge of the linear model of the system. This assumption is relaxed in the third and fourth schemes. In the third scheme, the controller is based on tracking the hub position while controlling the elastic deflection at the tip. The fourth scheme employs two neural networks, one of the neural networks define an appropriate output for feedback and the other neural network acts as an inverse dynamics controller. Simulation results for two single flexible-link manipulators and a two-link manipulator are presented.

Learning Control using Neural Networks A number of techniques can be used to design controllers for unknown linear systems. Typically, a standard model structure is used and then the parameters of controllers or plant models are adapted based on stability theory. On the other hand, the control of uncertain nonlinear systems is difficult for a number of reasons. First, it is not easy to find a suitable model structure for the nonlinear dynamics unlike linear systems where a standard form of the transfer function is available for an unknown system of a given order.

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