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

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 part attribute, coupling results, nonlinearities, parameter adaptations and unmodeled dynamics in this kind of manipulator all give a contribution to those problems. regulate suggestions that forget about those uncertainties and nonlinearities typically fail to supply passable closed-loop functionality. This monograph develops and experimentally evaluates a number of clever (neural community established) keep watch over options to deal with the matter of controlling the end-point of flexible-link manipulators within the presence of all of the aforementioned problems. to focus on the most concerns, a truly flexible-link manipulator whose hub shows a large amount of friction is taken into account for the experimental paintings. 4 assorted neural community schemes are proposed and carried out at the experimental test-bed. The neural networks are educated and hired as on-line controllers.

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Control of Flexible-link Manipulators Using Neural Networks

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The exact solution, however, can be approximated by the assumed modes method using a finite number of modes, i----1 where the ~i(x) are any flmctions that satisfy the boundary conditions. If the functions tPi(x) are selected to be a set of polynomials in x, the resulting frequencies of vibration are only approximations to the actual natural frequencies of the system. If the functions ~i(x) are chosen as the eigenfunctions r the frequencies of vibration equal the natural frequencies of the system.

As mentioned earlier, a certain o u t p u t can be defined so that the zero dynamics corresponding to this new output are stable. This output can be defined as the joint variable, but it will not yield an acceptable tip response for a relatively flexible robot. In this section, it is shown that by using the new o u t p u t that was defined in the previous chapter, the concept of feedback-error learning may be invoked to control the tip position of a flexible-link manipulator. 1. 2. 2. The CFC is used both as an ordinary feedback controller to guarantee asymptotic stability of the system during the learning period and as a "reference model" for the response of the controlled system.

X: . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . .. . . . . . { . . . . . . . . . . . . . . . 9 . . . : . : . . . . . . . . . : . . . . . . . . . . . . . . . . . . 6. Pole-zero map of the tip position transfer function 40 Gt(s). Chapter 3 Output Redefinition In this chapter, the output redefinition method used for the first two control schemes proposed in this monograph is explained. First, a brief introduction is given regarding the output redefinition approach for nonlinear systems.

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