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The object of this work is to determine the most suitable values of process and solution parameters for electrospinning of polyacrylonitrile (PAN) nanofibers including solution concentration, applied voltage, and working distance between the needle tip and the collector plate. To investigate the effects of those parameters on the fiber morphology, nanofiber mat samples were produced by changing the value of parameters systematically. The scanning electron microscope images of these samples were analyzed to realize the effects of these parameters on the nanofiber morphology. Our results demonstrate that the diameter of the fibers increases with increasing concentration. However, the diam-eter reduces as the applied voltage and working distance between needle tip and the collector increase up to a certain value. In addition to this, viscosity and applied voltage have a strong effect on the uniformity and morphology of the nanofibers. Moreover, a relationship between spinning distance, voltage supplied, solution concentration, charge density, bead formation, and the diameter of the electrospun PAN nanofiber were established in the study.

Increasing demands for higher operational speeds, the need for flexible machinery and recent developments in microchip technology have made programmable machine systems an attractive alternative to conventional systems. However, some difficulties still remain for proper control of programmable systems, especially at higher speeds. These can be categorized into two groups : trajectory planning and trajectory tracking. Conventional trajectory planning methods are ineffective for general application, especially when velocity and acceleration conditions are included. There are many mathematical functions but polynomials are shown to be the most versatile for trajectory planning: however, these can give curves with unexpected oscillations, commonly called meandering. Tracking of a motion in this situation could engender severe practical problems. In this study, a new interpolation method using polynomials with arbitrarypowers is proposed to overcome this disadvantage.

This paper presents the implementation of an explicit model reference adaptive control (MRAC) for position tracking of a dynamically unknown robot. An auto regressive exogenous (ARX) model is chosen to define the plant model and the control input is optimised in a H2 norm to reduce computational time and to simplify the algorithm. The theory of MRAC falls into a description of the various forms of controllers and parameter estimation techniques, therefore, applications may require very complicated solution methods depending on the selected laws. However, in this study, the proposed MRAC shows that applications may be as easy as classical control methods, such as PID, by guaranteeing the stability and achieving the convergency of the plant parameters. Despite the selected simple control model, simple optimisation method and drawbacks of the robot the experimental results show that MRAC provides an excellent position tracking compared with conventional control (PID). Many experimental implementations have been done on the robot and one of them is included in the paper.