*Role of Uncertainty in Model Development and Control Design for a Manufacturing Process DOI: http://dx.doi.org/10.5772/intechopen.104780*

where **zavg** is the average of the N noise signals. Eq. (18) demonstrates that a total number of N samples are required to reduce the signal noise level by ffiffiffiffi *N* <sup>p</sup> . Since our goal is to reduce the image noise to within a fixed threshold (a constant number expressed as a standard deviation), a smaller variation in the original image requires much less samples to make an equivalent noise-reduction estimation. Thus, it is worthwhile for the camera to move around, rather than being stationary, in order to find the best locations where the image noise level estimation is small. In general, we can reduce the noise level as much as needed by taking more samples.

### *3.2.4 The dynamic errors and its modeling in the feedback control loop*

The dynamic errors in a robot manipulator consist of any joint non-static errors. Among these error sources, which have the most significant effect on the robot position control accuracy, are the deviations between the actual joint rotation with its measured value from the unmodeled dynamic uncertainties, such as backlash, friction, compliance due to gears' elimination, joint or link flexibility and thermal effects. As discussed in Section 3.2.2, we can account for all these errors in the dynamic modeling step by developing a high-fidelity dynamic model, where all these parameter values could be identified through calibration. An easier way is to regard all these dynamic errors as disturbances to a manipulator control system. The control system is able to make the plant output track the desired input (the reference signal) and while simultaneously, it rejects these disturbances. The design of the robot manipulators control systems and the demonstrations of the capability of these feedback loops to reject these aforementioned dynamic errors are discussed in more detail in the later sections.
