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Home / programming language / Online Matlab Projects / Adaptive positioning control of an ultrasonic linear motor system
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Matlab Project - Power Electronics,final year projects.
  • Matlab Project - Power Electronics,final year projects.

Adaptive positioning control of an ultrasonic linear motor system

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SKU: PROJ7621 Categories: 2017 Projects, Final Year Projects, Online Matlab Projects, Power Electronics Projects, programming language Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Adaptive positioning control of an ultrasonic linear motor system

Abstract— A 3PRR parallel precision positioning system, driven by three ultrasonic linear motors, was designed for use as the object stage of a scanning electron microscope (SEM). To improve the tracking accuracy of the parallel platform, the positioning control algorithms for the drive joints needed to be studied. The dead-zone phenomenon caused by static friction reduces the trajectory tracking accuracy significantly. Linear control algorithms such as PID (Proportion Integration Differentiation) are unable to compensate effectively for the dead-zone nonlinearity. To address this problem, two types of feed forward compensation control algorithms have been investigated. One is constant feed forward with the integral separation PID; the other is adaptive feedback and feed forward based on the model reference adaptive control (MRAC). Simulations and experiments were conducted using these two control algorithms. The results demonstrated that the constant feed forward with integral separation PID algorithm can compensate for the time-invariant system after identifying the dead-zone depth, while the adaptive feedback and feed forward algorithm is more suitable for the time-varying system. The experimental results show good agreement with the simulation results for these two control algorithms. For the dead-zone nonlinearity caused by the static friction, the adaptive feedback and feed forward algorithm can effectively improve the trajectory tracking accuracy. < final year projects >

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