Development of Self-Tuning Control System with Fuzzy Compensation of Steady-State Error

2018 ◽  
Author(s):  
Liudmila Denisova ◽  
Vitalii Meshcheryakov
Keyword(s):  
Control System ◽  
Steady State ◽  
Self Tuning ◽  
State Error

On the Control System for Blank-Holder Force in Deep Drawing Based on Self-Tuning Fuzzy-PID Controller

2010 ◽  
Vol 97-101 ◽  
pp. 2995-3000
Author(s):  
Chun Ping Cao ◽  
Yu Sun
Keyword(s):  
Control System ◽  
Steady State ◽  
Dynamic Response ◽  
Pid Controller ◽  
Hydraulic Press ◽  
Fuzzy Pid ◽  
Blank Holder Force ◽  
Blank Holder ◽  
Fuzzy Pid Controller ◽  
Self Tuning

Blank-holder Force (BHF) control technology is an important guarantee for the quality of parts forming. Taking YJ28E—1000/1600Q hydraulic press as object, a hydraulic fuzzy control model based on self-tuning fuzzy-PID is built to deal with the undesirable dynamic response and the low steady-state precision of the hydraulic control system. The look-up table for optimal control parameters is generated from MATLAB Toolbox. A simulation study of the system shows that the dynamic response and steady-state precision is improved greatly by adopting this kind of self-adaptive fuzzy-PID controller.

scholarly journals Ball On Plate Balancing System Pada KIT Praktek PID Mata Kuliah Dasar Sistem Kendali

2017 ◽  
Vol 3 (3) ◽  
pp. 134
Author(s):  
Anwar Mujadin ◽  
Dwi Astharini
Keyword(s):  
Control System ◽  
Steady State ◽  
Rise Time ◽  
Input Image ◽  
Settling Time ◽  
Loop System ◽  
Close Loop System ◽  
Intelligent Control System ◽  
Arduino Uno ◽  
State Error

<p><em>Abstrak – </em><strong>Ball on plate adalah sistem pengendalian cerdas untuk mengarahkan bola  yang ada diatas plate sesuai dengan pola gerakan yang diinginkan tanpa menjatuhkan bola. Ball on plate ini digerakan oleh dua buah motor servo sebagai aktuator (keluaran) untuk menentukan posisi bola. Sedangkan kamera ditempatkan diatas plate sebagai sensor (masukan). Image yang ditangkap oleh kamera kemudian diolah oleh labview menjadi pixel posisi X dan Y. Kerjasama antar mikrokontroler Arduino Uno  dan Labview membentuk sebuah pengendalaian close loop system. Pada tulisan ini akan dibahas parameter penting dalam menganalisa  rise time, overshoot, settling time dan steady state error pada pengendalian sistem ball on plate menggunakan PID.</strong></p><p><strong><br /></strong></p><p><strong><em>Kata kunci </em></strong><em>- Arduino Uno R3 Ball on plate Controller,</em></p><p><em> </em></p><p><em>Abstract –</em> <strong>Ball on the plate is an intelligent control system to steer the ball over the plate that is in accordance with the desired pattern of movement without dropping the ball. Ball on plate is controlled  by two servo motors as actuators (output) to determine the position of the ball. While the camera is placed on the plate as a sensor (input). Image captured by the camera and processed by labview to pixel positions X and Y. The cooperation among the microcontroller Arduino Uno and Labview configurate a close loop system. In this paper will discuss important parameter in analyzing the rise time, overshoot, settling time and steady state error in the control system using PID ball on the plate.</strong></p><p><strong><br /></strong></p><strong><em>Keywords</em></strong><em> – Arduino Uno R3 Ball on plate Controller </em>

scholarly journals HYBRID CONTROL SYSTEM IN BIONIC LEG USING MYOWARE SENSOR

2021 ◽  
Vol 56 (4) ◽  
pp. 104-116
Author(s):  
W. Widhiada ◽  
M.A. Parameswara ◽  
I.G.N.N. Santhiarsa ◽  
I.N. Budiarsa ◽  
I.M.G. Karohika ◽  
...  
Keyword(s):  
Control System ◽  
Steady State ◽  
Hybrid Control ◽  
Persons With Disabilities ◽  
Hybrid Controller ◽  
Innovation Product ◽  
Programming Software ◽  
Hybrid Control System ◽  
System Time ◽  
State Error

A bionic robot leg (BRL) is a contrivance used to supersede a loss component of the lower limb due to amputation or congenital disability. Hybrid control of BRL is opted to obtain the maximum performance of BRL equipped with precise forms of kineticism and expeditious response by truncating the error and maximum overshoot and reducing time settle. This research aims to create a BRL innovation product for persons with disabilities at the Bali Puspadi Foundation. The novelty of this BRL is the implementation of the algorithm as outlined in the hybrid control system in the Arduino support package. The BRL utilizes a MyoWare sensor and an Arduino Mega 2560 microcontroller equipped with Matlab/Simulink R2020a programming software. The sensor is utilized to read the angular movement of the DC motor between 0 - 60° degrees and vice versa, following the concept of the gate cycle. The results obtained from the hybrid control simulation are 0.0713% on maximum overshoot, 0.0415% on steady-state error, and 1.292s on system time settle. Furthermore, the results obtained from the hybrid controller experiment are 0.627% on maximum overshoot, 0.257% on steady-state error, and 0.8s on system time settle.

Application of Fuzzy Controller with Self-Tuning Scaling Factors for the Filling System of Counter-Gravity Casting

2012 ◽  
Vol 538-541 ◽  
pp. 1122-1129
Author(s):  
Qiang Li ◽  
Qi Tang Hao ◽  
Wan Qi Jie
Keyword(s):  
Control System ◽  
Steady State ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Mold Filling ◽  
Scaling Factors ◽  
Gravity Casting ◽  
Input And Output ◽  
Self Tuning ◽  
Tube Filling

Pressurization control system plays an important role in the process control at the stages of riser-tube filling, mold-filling, pressure-increasing and pressure maintaining in counter-gravity casting. Casting quality is affected by the precision of pressurization control system to a great extent. Input and output scaling factors of conventional fuzzy controller are static, which is hard to ensure a suitable state for various counter-gravity casting equipments. A new fuzzy controller with self-tuning scaling factors has been developed in our laboratory, which will be described in this paper. Input and output scaling factors of fuzzy controller are tuned by different error ranges. When the differential pressure error is large, output scaling factor is increased to speed up dynamic response. At the same time, input scaling factors are also increased in order to reduce steady state error. Compared to fuzzy controller and conventional PID controller, fuzzy-PID controller with self-tuning scaling factors has more steady-state precision and excellent robustness. Experimental results show that at riser-tube filling stage and mold-filling stage the gas flow is stable, and the tracking property of pressurization control system is very satisfactory.

Spring Force Compensation for Bond Head

2011 ◽  
Vol 317-319 ◽  
pp. 1711-1716
Author(s):  
Lei Zhou ◽  
Jian Gang Li ◽  
Ze Xiang Li
Keyword(s):  
Control System ◽  
Steady State ◽  
High Precision ◽  
High Speed ◽  
Precision Control ◽  
Loop Control ◽  
Identification Method ◽  
Spring Force ◽  
Force Compensation ◽  
State Error

A bond head with spring bearing has steady-state error problem in position loop control. To maintain high speed and high precision control, a spring force compensator is proposed in this paper. Firstly, the system is modeled and the problem is stated. Then, a close loop identification method for the spring force is advised. The compensator is also modeled and compared with the traditional controller. In the experiment part, the result shows the new control system with spring force compensator works well.

scholarly journals Implementation of Fuzzy Logic Control System on Rotary Car Parking System Prototype

2018 ◽  
Vol 12 (2) ◽  
pp. 706
Author(s):  
Nanang Ismail ◽  
Iim Nursalim ◽  
Hendri Maja Saputra ◽  
Teddy Surya Gunawan
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Steady State ◽  
Fuzzy Control ◽  
Rise Time ◽  
Settling Time ◽  
Inference Model ◽  
Time Value ◽  
Parking System ◽  
State Error

Rotary car parking system (RCPS) is one of the effective parking models used in the metropolitan area because the mechanical parking system is designed vertically to conserve the land usage. This paper discussed the implementation of fuzzy logic with the Sugeno Inference Model on the RCPS miniature control system. The research started with kinematics analysis and a mathematical model was derived to determine the slot position and optimal power requirements for each condition. Furthermore, the Fuzzy Inference model used was the Sugeno Model, taking into account two variables: distance and angle. These two variables were selected because in the designed miniature RCPS there will be rotational changes of rotation and rotation in turn. Variable distance was divided into four clusters, such as Zero, Near, Medium and Far. While the angle variables were divided into four clusters as well, such as Zero, Small, Medium, and Big. The test results on a miniature RCPS consisting of six parking slots showed that fuzzy based control provided better results when compared to conventional systems. Step response on the control system without fuzzy control showed the rise time value of 0.58 seconds, peak time of 0.85 seconds, settling time of 0.89, percentage overshoot of 0.20%, and steady state error of 4.14%. While the fuzzy control system provided the rise time value of 0.54 seconds, settling time of 0.83 seconds, steady state error of 2.32%, with no overshoot.

Verifying and Simulating of the Novel Analytical Method of the Steady-State Error for Nonunity Feedback Control Systems

2011 ◽  
Vol 301-303 ◽  
pp. 1670-1675 ◽  
Author(s):  
Shann Chyi Mou
Keyword(s):  
Control System ◽  
Steady State ◽  
Feedback Control ◽  
Error Function ◽  
Step Response ◽  
Type Number ◽  
Feedback Control System ◽  
Feedback Control Systems ◽  
Definition Of ◽  
State Error

Traditionally, the first step to analyze the steady-state error of nonunity feedback control system is to convert the system into an equivalent unity feedback control system, and calculate the steady-state error by using the concept of type number for unity feedback control system. In 2011, Mou is based on the concept of old type number and the definition of new steady-state error functione(t)=(1/KH)r(t)-y(t) by Kuo to offer the method for calculating the steady-state error of nonunity feedback control system. In this paper, three typical examples will be analyzed by the calculation of step response and by the simulation of Matlab. Therefore, we can prove that the definition of new steady-state error function e(t)=(1/KH)r(t)-y(t) by Kuo and the concept of old type number are useful to solve the steady-state error of nonunity feedback control system.

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