Do my fuzzy logic correct and how to tune my PID properly using this system?

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Isyraf Izuddin 2022-5-31

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回答： Sam Chak 2022-6-1

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TempControl2.slx

This is my system for first transfer function

This is the second one both of my system does not achieve the desired result and the second transfer function are worse

this is the mf of the system

i include my simulink here hope anybody can help my Final year project

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Isyraf Izuddin 2022-6-1

this is the right parameter that i use

Isyraf Izuddin 2022-6-1

Alright my supervisor have finalised to use the first transfer function

how do i improve my response to achieve the 1 with no %os, fast rise time, good settling time

this is the new mf and rule that i use

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Sam Chak 2022-6-1

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Hi @Isyraf Izuddin

Plant #2

Since you didn't specify the performance criteria, the fuzzy system is designed with some improments as compared to the open-loop response of the original plant. In fact, it is very difficult to design a "pure" fuzzy system from the expert knowledge approach to satisfy the performance criteria. From the numerator polynomial and the characteristic polynomial, one can guess that the design process is not easy.

A SISO Sugeno Type-1 fuzzy system is designed. In the closed-loop system, the settling time is achieved under 300 s without overshoot. The original plant settles at 1,500 s.

Three Gaussian fuzzy sets are created for the fuzzy input with MF1 is gaussMF(Error, 0.03317, -0.146), MF2 is gaussMF(Error, 0.3098, 0.436), and MF3 is gaussMF(Error, 0.3286, 0.9175).

Three singleton fuzzy sets are created for the fuzzy output such that SGT1 is defined exactly as –144, SGT2 is defined exactly as 0.07204, and SGT3 is defined exactly as -0.009993. The Fuzzy Rules are given by

Rule 1: If Error is MF1, then Control Output is SG1.

Rule 2: If Error is MF2, then Control Output is SG2.

Rule 3: If Error is MF3, then Control Output is SG3.

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Control Systems Fuzzy Logic Toolbox Fuzzy Logic in Simulink

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