유전자-퍼지 논리를 사용한 도립진자의 제어

이상훈,박세준,양태규 한국정보통신학회 2001 한국정보통신학회논문지 Vol.5 No.5

본 논문에서는 유전자-퍼지 제어 알고리즘에 대하여 논의하고 그 성능을 평가하였다. 이 알고리즘은 퍼지 논리와 유전자알고리즘의 융합된 형태이며, 제어 대상으로는 도립진자 시스템을 모델링 하였다. 퍼지 제어기는 두 개의 입력과 한 개의 출력 변수를 설계하기 위해 적용되며, GA(Genetic Algorithm)는 퍼지 규칙과 소속 함수를 선택, 교차, 돌연변이의 진화 연산을 통해 최적화한다. 컴퓨터 시뮬레이션에 퍼지 제어의 경우 초기 함수 f(0.3, 0.3)일 때 최대 언더슈트가 $-5.0 \times 10^{-2}[rad]$, 최대 오버슈트가 $3.92\times10^{-2}[rad]$으로 측정되었으나, 유전자 퍼지 알고리즘의 경우 최대 오버슈트와 언더슈트가 각각 0.0[rad]으로 측정되었다. 또한 정상상태 도달시간이 퍼지제어의 경우 2.12[sec], 유전자-퍼지 알고리즘은 1.32[sec]로 비교적 안정적으로 나타났다. 컴퓨터 시뮬레이션으로 이 알고리즘을 도립진자 시스템에 적용시키고, 그 성능의 우수성과 효율성을 증명하였다. In this paper, Genetic-Fuzzy Algorithm for Inverted Pendulum is presented. This Algorithms is combine Fuzzy logic with the Genetic Algorithm. The Fuzzy Logic Controller is only designed to two inputs and one output. After Fuzzy control rules are determined, Genetic Algorithm is applied to tune the membership functions of these rules. To measure of performance of the designed Genetic-Fuzzy controller, Computer simulation is applied to Inverted Pendulum system. In the simulation, In the case of f[0.3, 0.3] Fuzzy controller is measured that maximum undershoot is $-5.0 \times 10^{-2}[rad]$, maximum undershoot is $3.92\times10^{-2}[rad]$ individually however, Designed algorithm is zero. The Steady state time is approximated that Fuzzy controller is 2.12[sec] and designed algorithm is 1.32[sec]. The result of simulation, Resigned algorithm is showed it's efficient and effectiveness for Inverted Pendulum system.

Fuzzy-Sliding Mode Control of a Polishing Robot Based on Genetic Algorithm

Go, Seok-Jo,Lee, Min-Cheol,Park, Min-Kyu The Korean Society of Mechanical Engineers 2001 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.15 No.5

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

비선형 공정을 위한 FCM 클러스터링 알고리즘 기반 퍼지 추론 시스템

박건준(Park, Keon-Jun),강형길(Kang, Hyung-Kil),김용갑(Kim, Yong-Kab) 한국정보전자통신기술학회 2012 한국정보전자통신기술학회논문지 Vol.5 No.4

본 논문에서는 비선형 공정을 퍼지 모델링하기 위해 FCM 클러스터링 알고리즘을 기반으로 하는 퍼지 추론 시스템을 소개한다. 비선형 공정에 대한 퍼지 규칙의 생성은 일반적으로 차원이 증가할수록 규칙의 수가 지수적으로 증가하는 문제를 가지고 있다. 이를 해결하기 위해, FCM 클러스터링 알고리즘을 이용하여 입력 공간을 분산 형태로 분할함으로써 퍼지 모델의 규칙을 생성한다. 퍼지 규칙의 전반부 파라미터는 FCM 클러스터링 알고리즘에 의한 소속행렬로 결정된다. 퍼지 규칙의 후반부는 다항식 함수의 형태로 표현되며, 각 규칙의 후반부 파라미터들은 표준 최소자승법에 의해 동정된다. 마지막으로, 비선형 공정의 특성 및 성능을 평가하기 위하여 비선형 공정으로는 널리 이용되는 데이터를 이용한다. In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

퍼지 클러스터링 기반 퍼지뉴럴네트워크 설계 및 적용

박건준(Park, Keon-Jun),이동윤(Lee, Dong-Yoon) 한국산학기술학회 2013 한국산학기술학회논문지 Vol.14 No.1

본 논문에서는 FCM 클러스터링 알고리즘을 기반으로 하는 퍼지뉴럴네트워크를 제안한다. 일반적으로, 퍼지 규칙을 생성할 때 차원이 증가하면 퍼지 규칙의 수가 기하급수적으로 증가하는 문제를 가지고 있다. 이를 해결하기 위해, 제안된 네트워크의 퍼지 규칙은 FCM 클러스터링 알고리즘을 이용하여 입력 공간을 분산 형태로 분할함으로써 생성한다. 퍼지 규칙의 전반부 파라미터는 FCM 클러스터링 알고리즘에 의한 소속행렬로 결정된다. 퍼지 규칙의 후반 부는 다항식 함수의 형태로 표현되며, 퍼지뉴럴네트워크의 학습은 뉴런의 연결을 조절함으로써 실현되고, 오류 역전 파 알고리즘에 의해 행해진다. 마지막으로, 제안된 네트워크는 비선형 공정으로의 적용을 통해 성능을 평가한다. In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.

Optimal Design of a 2-Layer Fuzzy Controller Using the Schema Co-Evolutionary Algorithm

Byun, Kwang-Sub,Sim, Kwee-Bo Korean Institute of Intelligent Systems 2004 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.4 No.3

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

Optimal Design of a 2-Layer Fuzzy Controller Using the Schema Co-Evolutionary Algorithm

Kwang-Sub Byun,Kwee-Bo Sim 한국지능시스템학회 2004 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.4 No.3

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

Genetic Algorithm을 기반을 둔 사용자에 따른 NPC의 FuSM 동적 행동패턴 생성기법

김동주,김정윤 차세대컨버전스정보서비스학회 2016 차세대컨버전스정보서비스기술논문지 Vol.5 No.1

게임에서는 그래픽, 스토리, 난이도 등 다양한 재미 요소를 가지고 있으며, 특히 난이도는 게임을 처음 접하는 초보자, 게임을 능수능란하게 하는 숙련자 모두에게 적합한 난이도가 존재한다. 본 논문에서는 FSM에 Fuzzy Algorithm를 접목시킨 FuSM과 다윈의 진화론을 토대로 한 Genetic Algorithm을 기반으로 Player(사용자)에 따른 난이도를 조절하는 알고리즘을 제안한다. Genetic Algorithm을 활용해 Player의 성적을 기준으로 실시간 동적 난이도 조절을 하는 알고리즘이다. 이를 통해 Player의 몰입도를 증진시킨다. A game is comprised of various fun elements such as graphic, story and the degree of difficulty. Specifically, the element of the game is of different level between the game beginners and the skilled players. In this study, we propose an algorithm for adjusting the degree of difficulty based on the player (user) using the Genetic Algorithm based on a theory of Darwin's survival and the FuSM which is a combination of Fuzzy Algorithm and FSM. The utilization of the Genetic Algorithm based on the results of the player is an algorithm for adjusting the dynamic degree of difficulty in real time. As a result, the proposed algorithm enhances the immersion of a player.

퍼지 알고리즘을 이용한 정풍량 공조기의 고장 감지 및 진단

한도영,김진 대한설비공학회 2005 설비공학 논문집 Vol.17 No.5

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of an air-conditioning system. In this study, partial faults for fans, coils, dampers, and sensors of a constant volume air handling unit were considered. A fuzzy algorithm was developed to detect and diagnose these faults. Diagnostic results by the fuzzy algorithm were compared with those by the model reference algorithm. The fuzzy algorithm showed better results in diagnostic accuracies.

지능제어기 보상을 위한 유전 알고리즘 이용에 관한 연구

신위재,문정훈,Shin, Wee-Jae,Moon, Jeong-Hoon 한국융합신호처리학회 2009 융합신호처리학회 논문지 (JISPS) Vol.10 No.1

The fuzzy control, neural network and genetic algorithm(GA) are algorithms to make the intelligence of system more higher. In this paper, we optimized the fuzzy controller using a genetic algorithm for desire response. Also a compensated fuzzy controller has dual rules. One control rule used to decrease the overshoot and rise time occurring in transient response region and another fuzzy control rule use to decrease the steady state error and rapildy to converge at the convergence region. GA is necessary to optimal the exchange time of the two fuzzy control rule base. Fuzzy-GA controller have a process of reproduction, crossover and mutation and we experimented by hydraulic servo motor control system We could observe that compensated Fuzzy-GA controller have good control performance compare to the fuzzy control technique have two rule base table.

On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm

Zong-Yi Xing,Yong Zhang,Yuan-Long Hou,Li-Min Jia 대한전기학회 2007 International Journal of Control, Automation, and Vol.5 No.4

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-Ⅱ algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.