A fuzzy logic controller design and simulation for a sawmill bucking system Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/mg74qp62m

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  • This document investigates, justifies, designs, and simulates the application of a fuzzy logic controller/optimizer in a sawmill bucking system application that is the first production station where stems coming from the mountains are processed in a sawmill. One of the areas of success in the application of fuzzy logic is in control systems. Fuzzy logic controllers are based on approximate reasoning of crisp (physical) variables to provide a possible output. Fuzzy controllers' decisions, if appropriate, try to simulate common sense decisions that a human will make after evaluating a set of data. Fuzzy control systems are designed with the intention of replacing an expert operator with a ruled-based system. In this specific application the stems are sorted out in the sawmill yard as stems for Mill "A", Mill "B" and Green End before they arrive at the bucking station. Mill "A" is a dimensional sawmill, Mill "B" is a stud sawmill and Green End is a Veneer mill. After the bark is removed from the tree, it passes through a bucking station where there are a length, and a diameter measurement system to provide an operator with some stem information so he/she can make a decision on what blocks he/she should cut. The operator bucking solutions are based on his/her and other operators past experiences, the data provided by the length and diameter measurement systems and his/her visual inspection on the shape (sweep & taper) of the stem. The decisions of the operator are not exact but good enough or appropriate. His/her decisions have a common goal that is to recover the most wood out of every tree. This type of bucking system has been in practice for at least 50 years, and therefore, there is a very well established set of rules applied by the operator to obtain the most wood out of every tree. The fuzzy logic controller simulations were designed with three fuzzy controller engines, one engine for each mill. This was due to the computing time and the simplicity of the design. Each one of the fuzzy controllers mentioned above was simulated using Matlab with the Fuzzy Logic Toolbox, and Simulink until the required design specifications were met.
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  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-08-27T20:11:13Z (GMT) No. of bitstreams: 1 DelgadoFidel2001.pdf: 7005876 bytes, checksum: 24d4daba01fda481a0f8587b0320c44d (MD5)
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