A fuzzy logic control system for a friction stir welding process
- Authors: Majara, Khotso Ernest
- Date: 2006
- Subjects: Friction welding , Fuzzy logic , Automatic control , Fuzzy systems
- Language: English
- Type: Thesis , Masters , MTech
- Identifier: vital:9594 , http://hdl.handle.net/10948/405 , Friction welding , Fuzzy logic , Automatic control , Fuzzy systems
- Description: FSW is a welding technique invented and patented by The Welding Institute in 1991. This welding technique utilises the benefits of solid-state welding to materials regarded as difficult to weld by fusion processes. The productivity of the process was not optimised as the real-time dynamics of the material and tool changes were not considered. Furthermore, the process has a plastic weld region where no traditional modelling describing the interaction between the tool and work piece is available. Fuzzy logic technology is one of the artificial intelligent strategies used to improve the control of the dynamics of industrial processes. Fuzzy control was proposed as a viable solution to improve the productivity of the FSW process. The simulations indicated that FLC can use feed rate and welding speed to adaptively regulate the feed force and tool temperature respectively, irrespective of varying tool and material change. The simulations presented fuzzy logic technology to be robust enough to regulate FSW process in the absence of accurate mathematical models.
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- Date Issued: 2006
Intelligent gripper design and application for automated part recognition and gripping
- Authors: Wang, Jianqiang
- Date: 2002
- Subjects: Automatic control , Robots, Industrial , Robotics
- Language: English
- Type: Thesis , Doctoral , DTech (Engineering)
- Identifier: vital:10816 , http://hdl.handle.net/10948/102 , Automatic control , Robots, Industrial , Robotics
- Description: Intelligent gripping may be achieved through gripper design, automated part recognition, intelligent algorithm for control of the gripper, and on-line decision-making based on sensory data. A generic framework which integrates sensory data, part recognition, decision-making and gripper control to achieve intelligent gripping based on ABB industrial robot is constructed. The three-fingered gripper actuated by a linear servo actuator designed and developed in this project for precise speed and position control is capable of handling a large variety of objects. Generic algorithms for intelligent part recognition are developed. Edge vector representation is discussed. Object geometric features are extracted. Fuzzy logic is successfully utilized to enhance the intelligence of the system. The generic fuzzy logic algorithm, which may also find application in other fields, is presented. Model-based gripping planning algorithm which is capable of extracting object grasp features from its geometric features and reasoning out grasp model for objects with different geometry is proposed. Manipulator trajectory planning solves the problem of generating robot programs automatically. Object-oriented programming technique based on Visual C++ MFC is used to constitute the system software so as to ensure the compatibility, expandability and modular programming design. Hierarchical architecture for intelligent gripping is discussed, which partitions the robot’s functionalities into high-level (modeling, recognizing, planning and perception) layers, and low-level (sensing, interfacing and execute) layers. Individual system modules are integrated seamlessly to constitute the intelligent gripping system.
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- Date Issued: 2002
Methods for designing and optimizing fuzzy controllers
- Authors: Swartz, Andre Michael
- Date: 2000
- Subjects: Fuzzy sets , Fuzzy systems , Automatic control
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5412 , http://hdl.handle.net/10962/d1005226 , Fuzzy sets , Fuzzy systems , Automatic control
- Description: We start by discussing fuzzy sets and the algebra of fuzzy sets. We consider some properties of fuzzy modeling tools. This is followed by considering the Mamdani and Sugeno models for designing fuzzy controllers. Various methods for using sets of data for desining controllers are discussed. This is followed by a chapter illustrating the use of genetic algorithms in designing and optimizing fuzzy controllers.Finally we look at some previous applications of fuzzy control in telecommunication networks, and illustrate a simple application that was developed as part of the present work.
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- Date Issued: 2000