<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In automation and mechatronics applications, mass spring damper system (MSDS) plays a significant role in ensuring model serviceability and safety. The system’s dynamic of this mechanical system is quite challenging to control. In this paper, the system is a single degree of freedom (SDOF) spring mass system. The issue of performance evaluation of three controllers, linear proportional integral derivate (LPID), nonlinear PID (NPID), and fuzzy logic controller (FLC), is presented. FLC demands that experience be built on information based on a rule. It has two inputs, one of which is the displacement and the other is the velocity, and force is calculated for forced damped vibration control for a single degree of freedom system. The method used for defuzzification is the center of gravity (COG). However, effective control of a system depends largely on the accuracy of the mathematical model that predicts its dynamics behavior. The mathematical model for the MSDS is based on a set of nonlinear second-order ordinary differential equations to simulate the dynamic accurately. The proposed control schemes are implemented with the aid of MATLAB and SIMULINK to investigate the system performance. The sequence of the three controllers in terms of performance is as follows: the first of them is FLC and then in some dominant cases this is NPID by comparison with the LPID and one without any controller cases. Also, FLC displays more effectiveness and efficiency than the system without this controller after a comparative of system analysis performance evaluation.</p>
</abstract>

In automation and mechatronics applications, mass spring damper system (MSDS) plays a significant role in ensuring model serviceability and safety. The system’s dynamic of this mechanical system is quite challenging to control. In this paper, the system is a single degree of freedom (SDOF) spring mass system. The issue of performance evaluation of three controllers, linear proportional integral derivate (LPID), nonlinear PID (NPID), and fuzzy logic controller (FLC), is presented. FLC demands that experience be built on information based on a rule. It has two inputs, one of which is the displacement and the other is the velocity, and force is calculated for forced damped vibration control for a single degree of freedom system. The method used for defuzzification is the center of gravity (COG). However, effective control of a system depends largely on the accuracy of the mathematical model that predicts its dynamics behavior. The mathematical model for the MSDS is based on a set of nonlinear second-order ordinary differential equations to simulate the dynamic accurately. The proposed control schemes are implemented with the aid of MATLAB and SIMULINK to investigate the system performance. The sequence of the three controllers in terms of performance is as follows: the first of them is FLC and then in some dominant cases this is NPID by comparison with the LPID and one without any controller cases. Also, FLC displays more effectiveness and efficiency than the system without this controller after a comparative of system analysis performance evaluation.

Dynamical analysis and controllers performance evaluation for single degree-of-freedom system

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

In automation and mechatronics applications, mass spring damper system (MSDS) plays a significant role in ensuring model serviceability and safety. The...

Symbol	Value	Unit
m	2	kg
k	250	N/m
b	2.236	–

Case 3
Type	Absolute maximum displacement (m)	Reduction (%)	Absolute maximum velocity (m/s)	Reduction (%)
Without controller	8.465 × 10⁻³	–	1.206 × 10⁻¹	–
With controller
FLC	1.689 × 10⁻²	99.527%	1.99 × 10⁻¹	65.008%
LPID	1.070 × 10⁻²	26.402%	1.229 × 10⁻¹	1.907%
NPID	6.205 × 10⁻³	26.698%	5.640 × 10⁻³	95.323%

Dynamical analysis and controllers performance evaluation for single degree-of-freedom system

Article Category: Research-Article

Published Online: Aug 12, 2020

Page range: 1 - 12

Received: Jul 02, 2020

DOI: https://doi.org/10.21307/ijssis-2020-018

Keywords
FLC, LPID, NPID, MSDS, SDOF, Mamdani system, Defuzzification

© 2020 Ivan Isho Gorial published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Values of system parameters.

Response of SDOF system and percentage reduction in response output.

Values of gains.

Dynamical analysis and controllers performance evaluation for single degree-of-freedom system

Article Category: Research-Article

Published Online: Aug 12, 2020

Page range: 1 - 12

Received: Jul 02, 2020

DOI: https://doi.org/10.21307/ijssis-2020-018

KeywordsFLC, LPID, NPID, MSDS, SDOF, Mamdani system, Defuzzification

© 2020 Ivan Isho Gorial published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Figure 17:

Values of system parameters.

Response of SDOF system and percentage reduction in response output.

Values of gains.

Keywords
FLC, LPID, NPID, MSDS, SDOF, Mamdani system, Defuzzification