Programmable logic controller based variable speed drives for educational trainer

The PLC based motor control system is the key area of concerned to relate PLC to the real industrial environment. However, there is no PLC based industrial motor control trainer available in the Automation lab of Politeknik Kota Kinabalu for the practical purposes. This has initiated the need to develop a research and product on the title of “Programmable Logic Controller Based Variable Speed Drives For Educational Trainer”. This research focused on VSD controlled by PLC conventional programming and Fuzzy Logic based PLC programming. A prototype “Two Conveyors Packaging System” has been constructed. This application is to synchronize two conveyors so that parts and packaging boxes are positioned correctly, regardless of the part and package box positions and the speed of conveyor. Several PLC programs were developed individually for sectionals of the prototype application; the input devices photoelectric part sensors (P004A), motor encoders E1 and E2 (P004B) and output device is VSD for box conveyor M2 (P004E). All these programs can work independently; subsequently to be combined to control the whole prototype application with additional PLC program on conventional basis, and fuzzy logic basis (P004C and P004D). These step by step programming methods contributed to the 10 experiments procedures to achieve the objective to construct the educational trainer procedures. As a conclusion, this research has achieved the objectives to construct the educational trainer procedures to implement PLC conventional and fuzzy logic based programming to control a motor driven by VSD, based on the concept of Prototype Two Conveyor Packaging System

Fuzzy Logic Engine

The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language

CAR TRACTION CONTROL SYSTEM

This project explores the potential of implementing fuzzy logic algorithm for traction control system using VHDL. Previously, the project on car traction control was done by simulation using fuzzy logic approach. The Fuzzy Logic Toolbox in MATLAB software is used to create simulation for fuzzy logic system. The challenge of the project is to design the control system using hardware description language for future implementation on hardware using FPGA. Fuzzy logic controller provides optimum control according to the conditions specify. It is useful when the driving condition is uncontrolled. The core programming language which will be used as the hardware description language is VHSIC Hardware Description Language (VHDL). VHDL is used in FPGA - based implementation. The methodology includes designing the fuzzy logic controller, development of the algorithm and codes programming. After that, the following phase includes testing and troubleshooting. Lastly, carry out the documentation. In conclusion, it is possible to develop the algorithm for fuzzy - based car traction control system using VHDL. The implementation of the control system using VHDL is viable for future implementation onto FPGA. Thus the performance of the car traction control would be enhance

Line and wall follower hexapod robot

Robot widely use to help human to do something, especially for difficult or danger task. To fulfil the robot requirements, some techniques, sensors and controller have been applied. Due to kind of robot is a hexapod robot, which it develops in this research. Hexapod robot is a mechanical vehicle that’s walk on 6 legs. A hexapod robot movement are guided with guidance, they are line and wall. Fuzzy logic control as intelligent control is applied to govern the robot follow line and wall. Fuzzy logic controller is used to create a smooth response of robot behaviour rather than logic programming. Infrared sensors are used to sense line and distance to the wall as the input variable for the controller. Based on these signals, the controller control the turning angle of forward movement thus making robot to move forward and turning in same time

Aggregated fuzzy answer set programming

Fuzzy Answer Set programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics

A logic programming framework for possibilistic argumentation: formalization and logical properties

In the last decade defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning. The logic programming paradigm has shown to be particularly useful for developing different argument-based frameworks on the basis of different variants of logic programming which incorporate defeasible rules. Most of such frameworks, however, are unable to deal with explicit uncertainty, nor with vague knowledge, as defeasibility is directly encoded in the object language. This paper presents Possibilistic Logic Programming (P-DeLP), a new logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty. Such features are formalized on the basis of PGL, a possibilistic logic based on G¨odel fuzzy logic. One of the applications of P-DeLP is providing an intelligent agent with non-monotonic, argumentative inference capabilities. In this paper we also provide a better understanding of such capabilities by defining two non-monotonic operators which model the expansion of a given program P by adding new weighed facts associated with argument conclusions and warranted literals, respectively. Different logical properties for the proposed operators are studie