Neuro-Fuzzy Control of Inverted Pendulum System for Intelligent Control Education

Geun Hyung Lee,Seul Jung 한국지능시스템학회 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.4

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

저온저장고의 뉴로-퍼지 제어시스템 개발

양길모,고학균,홍지향 한국농업기계학회 2003 바이오시스템공학 Vol.28 No.2

The study was conducted to develop precision control system for cold storage facility that could offer safe storage environment for green grocery. For that reason, neuro-fuzzy control system with learning ability algorithm and single chip neuro-fuzzy micro controller was developed for cold storage facility. Dynamic characteristics and hunting of neuro-fuzzy control system were far superior to on-off and fuzzy control system. Dynamic characteristics of temperature were faster than on-off control system by 1,555 seconds(123% faster) and fuzzy control system by 460 seconds(36.4% faster). When system, and ±0.4˚C in fuzzy control system, and ±0.3˚C in neuro-fuzzy control system. Hunting of humidity and wind velocity was also controlled precisely by 70 to 72.5% and 1m/s For storage experiment with onion, characteristics of neuro-fuzzy control system were tested. Dynamic characteristics of neuro-fuzzy control system made cold storage facility conducted precooling ability and minimized hunting.

Neuro-Fuzzy Control of Inverted Pendulum System for Intelligent Control Education

G.H.Lee,정슬 한국지능시스템학회 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.4

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

Structure Identification of a Neuro-Fuzzy Model Can Reduce Inconsistency of Its Rulebase

Bo-Hyeun Wang,Hyun-Joon Cho 한국지능시스템학회 2007 한국지능시스템학회논문지 Vol.17 No.2

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems of fuzzy system reproduction and nonlinear system modeling in order to validate our claim.

Neuro-Fuzzy Control of Inverted Pendulum System for Intelligent Control Education

Lee, Geun-Hyung,Jung, Seul Korean Institute of Intelligent Systems 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.4

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

A Model-free Output Feedback Adaptive Optimal Fuzzy Controller for LC-filtered Three-phase Voltage Source Inverters

Nam Hai Trinh,Loc Ong Xuan,Nga Thi-Thuy Vu,Anh Tuan Nguyen 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.6

This paper proposes a model-free output feedback control-based adaptive fuzzy controller using a current-sensorless configuration for LC-filtered three-phase voltage source inverters (VSIs). The proposed adaptive fuzzy scheme is constructed of three parts: an adapter, an adaptive optimal fuzzy controller, and an adaptive optimal fuzzy identifier. The adapter is designed based on an adaptive neuro-fuzzy inference system (ANFIS) network which uses the error between the system output and identifier output as an input to generate the online updated parameters. Next, both the adaptive fuzzy controller and the fuzzy identifier are designed based on the Takagi-Sugeno (T-S) fuzzy model. In particular, the proposed algorithm is robust against external disturbance and parameter uncertainties due to not requiring the system parameters. Moreover, the proposed scheme uses a current-sensorless configuration, which reduces the system complexity and cost. Both the stability of the proposed method and the convergence of adapted parameters are completely assured by using the Lyapunov stability theory. Finally, the effectiveness of the proposed adaptive fuzzy controller is verified through simulation in comparison with a conventional T-S fuzzy controller. The results show that the proposed model-free output feedback control-based adaptive fuzzy controller yields better control performance, such as faster transient response, smaller steady-state error, and lower total harmonic distortion (THD) under the change of load (step changes of linear load, unbalanced load, and nonlinear load), parameter variations, and input disturbances.

합 기반의 전건부를 가지는 뉴로-퍼지 시스템 설계

한창욱,이돈규 한국정보처리학회 2024 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.13 No.2

본 논문에서는 규칙의 수를 줄여 간결한 지식 기반을 보장할 수 있는 합 기반의 전건부를 가지는 뉴로-퍼지 제어기를 제안하였다. 제안된 뉴로-퍼지 제어기는 모든 입력 변수의 AND 조합을 전건부로 하는 구조의 퍼지 규칙보다 더 큰 입력 영역을 커버하기 위해 전건부에 입력 퍼지 집합의 합집합 연산을 허용하였다. 이러한 뉴로-퍼지 제어기를 구성하기 위해 본 논문에서는 OR 및 AND 퍼지 뉴런으로 구성된 multiple-term unified logic processor (MULP)를 고려하였다. 이러한 OR 및 AND 퍼지 뉴런은 조정 가능한 연결 강도 집합을 가지므로 학습을 통하여 최적의 연결 강도 집합을 찾을 수 있다. 초기 최적화 단계에서 유전 알고리즘은 제안된 뉴로 퍼지 제어기의 최적화된 이진 구조를 구성하고, 이후 확률에 기반한 강화 학습은 성능 지수를 더욱 향상시켜서 유전 알고리즘에 의해 최적화된 제어기의 이진 연결을 개선하였다. 역진자 시스템을 제어하기 위한 모의실험 및 실험을 통해 제안된 방법의 유효성을 검증하였다. In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reducednumber of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to coverbigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of ORand AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while thestochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of theperformance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation andexperiment.

심박변이도를 이용한 적응적 뉴로 퍼지 감정예측 모형에 관한 연구

박성수,이건창 한국디지털정책학회 2019 디지털융복합연구 Vol.17 No.1

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system. 감정을 정확히 예측하는 것은 환자중심의 의료디바이스 개발 및 감성관련 산업에서 매우 중요한 이슈이다. 감정 예측에 관한 많은 연구 중 감정 예측에 심박 변동성과 뉴로-퍼지 접근법을 적용한 연구는 없다. 본 연구는 HRV를 이용한 ANFEP(Adaptive Neuro Fuzzy system for Emotion Prediction)을 제안한다. ANFEP의 핵심 기능은 인공 신경망과 퍼지 시스템을 통합해 예측 모델을 학습하는 ANFIS(Adaptive Neuro-Fuzzy Inference System)에 기반한다. 제안 모형의 검증을 위해 50명의 실험자를 대상으로 청각자극으로 감정을 유발하고, 심박변이도를 구하여 ANFEP 모형에 입력하였다. STDRR과 RMSSD를 입력으로 하고 입력변수 당 2개의 소속함수로 하는 ANFEP모형이 가장 좋은 결과를 나타났다. 제안한 감정예측 모형을 선형회귀 분석, 서포트 벡터 회귀, 인공신경망, 랜덤 포레스트와 비교한 결과 본 제안모형이 가장 우수한 성능을 보였다. 연구 결과는 보다 적은 입력으로 신뢰성 높은 감정인식이 가능함을 입증했고, 이를 활용해 보다 정확하고 신뢰성 높은 감정인식 시스템 개발에 대한 연구가 필요하다.

Vehicle License Plate Image Segmentation System Using Cellular Neural Network Optimized by Adaptive Fuzzy and Neuro-Fuzzy Algorithms

Basuki Rahmat,Endra Joelianto,I Ketut Eddy Purnama,Mauridhi Hery Purnomo 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.12

Vehicle License Plate Images Segmentation is a substantial stage for developing an Automatic License Plate Recognition (ALPR) system. In this paper, it is considered an efficient segmentation algorithm for extracting vehicle license plate images using Cellular Neural Networks (CNN). The learning CNN templates values are formulated as an optimization problem to achieve the desired performances which can be found by means of Adaptive Fuzzy (AF) algorithm and Neuro-Fuzzy (NF) algorithm techniques. The main objective of the paper is to compare the performances of standard CNN, Adaptive Fuzzy (AF), and Neuro-Fuzzy (NF) on real data of several vehicle license plate images of standard Indonesia License Plates. The results are then compared with ideal vehicle license plate images. Quantitative analysis between ideal vehicle license plate images and segmented vehicle license plate images is presented in terms of Peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). From the performance analysis, the CNN template optimized by ANFIS algorithm is more recommended than the standard CNN edge detector or the CNN template optimized by Adaptive Fuzzy algorithm in vehicle license plate image segmentation. It is shown from the calculation that PSNR is 80% better than the standard CNN, and the resulted MSE and RMSE are 70% better than the standard CNN. Whereas the CNN template optimized by Adaptive Fuzzy algorithm achieves the PSNR 90% better than the standard CNN, but it yields the MSE and RMSE 40% worse than the standard CNN.

A Neuro-Fuzzy Inference System for Sensor Failure Detection Using Wavelet Denoising, PCA and SPRT

Na, Man-Gyun Korean Nuclear Society 2001 Nuclear Engineering and Technology Vol.33 No.5

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.