Q5. Fuzzy Logic and PLC Ladder Diagram a) Develop a fuzzy inference system for g
ID: 3840112 • Letter: Q
Question
Q5. Fuzzy Logic and PLC Ladder Diagram
a) Develop a fuzzy inference system for grading a robotics course. The inputs are your effort level(low and high) in the course and your test grade (poor, moderate, and good.) The output is your letter grade (A, B, C,D and F). Write the rule base for the system.
b) Two different motors need to be controlled using a Programmable Logic Controller (PLC). A push button switch turns on the first motor. This Motor needs to be turned off after 60 seconds using a timer. 60 seconds after the first motor turns off, a second motor needs to be turned on. This motor needs to be turned off after 120 seconds using a timer.
Draw the ladder diagram to implement this control sequence.
The inouiu i a) Develop a level (low and high) in the cour He and vour eNan urade ooor moderate, and woody Ilie euiput is ,vour letter grade CA, B, C, and I') write the rule bane tor the ayutem Ivo dineront motorw need to be eontolled uning a Programmable Logic Controller N. b) push button Nwitch turnN on the motor, This motor needs to be turned oil afler 60 seconds, using a timer oo seconda aner the (ITH motor turns o11, a Recond motor needs to be urned on. This motor needs to be turned ofT aner 120 Heconds using a timer, Draw the ladder diagram to implement this control Hequence,Explanation / Answer
Fuzzy logic was first proposed in by Lotfi A. Zadeh in 1965. Since then Fuzzy logic has
emerged as a powerful technique for the controlling industrial processes, household and entertainment
electronics, diagnosis systems and other expert systems. Rapid growth of this technology has actually
started from Japan and then spread to the USA and Europe. Fuzzy logic is extremely useful in
applications where we have a complex process and because of nonlinearities or time-varying
responses, it is impossible to mathematically model the process. Often, traditional control methods
such as PID control can’t provide adequate control for these types of applications. Typically, these
processes are still controllable by using and applying the expert knowledge of operators who have
learned how the process responds to various input conditions [4]. The most common industrial control
systems are Distributed Control System (DCS) and PLC’s. DCS is a computerized control system used
to control the production lines in the industry as oil refining plants, chemical plants, pharmaceutical
manufacturing, etc. where continues control (PID loops) is dominating. PLC systems were typical for
discrete (event) control – automotive, electronics, etc. Their primary goal was to replace the relay
technology. Nowadays they have wide instruction libraries including function block for continues
control (well-designed PID, lead-lag blocks, etc.) but there are missing libraries for intelligent control
(fuzzy systems and neural networks). The proposed paper will summarize some existing fuzzy
toolboxes for PLCs and present a universal fuzzy system for PLC with a methodology to convert
Matlab fuzzy system into PLCs fuzzy structure.
2 Review of existing fuzzy toolboxes for PLC systems
RSLogix5000 Fuzzy Designer
RSLogix5000 Fuzzy Designer - software package from Allen - Bradley designed for the
creation of fuzzy systems and hierarchical fuzzy systems. It is mainly used in the following
applications:
- Industrial automation and control systems (controller, supervisor, process model).
- Process diagnostics and intelligent monitoring systems (process state classification).
- The process of decision-making and forecasting (decision support systems).
- Forecasting (prediction model).
Fuzzy Designer provides a function block-type environment to create your fuzzy logic
algorithms. Simple point and click interfaces make it easy to define, for example, the fuzzification
membership functions (MF) or the rule block definitions. Fuzzy Designer provides a set of built-in
components which allows to easily building hierarchical fuzzy systems. Once fuzzy logic algorithm
was created, Fuzzy Designer integrates it with Logix controllers by using the new add-on instruction
feature in RSLogix 5000 software
SIMATIC S7 Fuzzy Control
The S7 Fuzzy Control software package consists of two individual products:
- The product Fuzzy Control mainly contains the control block (function block - FB) and the
data block (DB).
- The product Configuration Fuzzy Control contains the tool for configuring the control block.
The FB is already prepared in its full range of performance and with all algorithms for
configuration and assigning parameters. A user-friendly tool is available for the configuration and
parameter assignment of this function block (Fig.2). Fuzzy controllers are easy to configure on the
basis of Fuzzy Control because their functionality is limited to the definition and execution of core
functions in fuzzy theory. An instance data block in the CPU of the programmable controller forms the
interface between the function block, the configuration tool, and the user. It’s possible to download a
number of fuzzy applications to a CPU and run them there. Each application is stored in a separate
data block; the number of the data block can be freely assigned
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