الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Production rate plays an important role in the industrial applications that mean higher demands for accelerations and speeds of the systems. Using belts for high precision applications has become appropriate due to the rapid development in motor and drive technology. Belt drive systems provide high-speed acceleration, accurate and repeatable motion with a high efficiency, and low cost. The requirements for accuracy and repeatability are also increasing. A solution for these demands is high-speed linear belt drive. However, the drawbacks of such system are non-linear friction and flexibility of the belt. In addition, some parameters may be unknown or changeable with time, which make the precision control of this system difficult.
In the control system, the plant, which is displaying nonlinearities, has to be described accurately in order to design an effective controller. Obtaining the model, the designer has to follow one of two ways. First way is using the laws of physics, chemistry, biology and the other sciences to describe the equations of motion with Newton’s laws, or electric circuits and motors with Ohm’s or Kirchhoff’s laws depending on the plant of interest. This is generally called mathematical modeling. The second way requires the experimental data obtained by exciting the plant, and measuring its response. This is called system identification. It is preferred in the cases where the plant or process involves extremely complex physical phenomena or exhibits strong nonlinearities.
This thesis discusses the process of modeling a Belt-drive system to achieve precise position tracking during high-speed and high-acceleration motion. A model based on tracking control design is used to compensate inertia and friction. Friction phenomena and position dependent elasticity of the belt are analyzed. Computer simulated results show that the developed model is adequate. The PlO control for accurate tracking control and accurate position control is designed. The simulation results demonstrate that the designed controller meets the specified performance specifications. New technique is proposed for tuning the feedback gains of the control system for optimal performance.
Obtaining mathematical model for a system may be rather complex and time consuming, as it often requires some assumptions such as defining an operating point and ignoring some system parameters, etc. This fact has recently led researchers to exploit the neural and fuzzy techniques in mode ling complex systems utilizing solely the input¬output data sets. Fuzzy logic allows one to model a system using a human knowledge and experience with if - then rules and can be efficiently coded but there is a need for tuning the membership functions effectively. Classical fuzzy logic controller is introduced to control the belt drive system. An automatic fuzzy PlO tuning for accurate position and tracking control have been studied with good results compared with classical PID controllers.