• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권1호
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• pp.22-29
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• 2007

Two fuzzy controllers defined by 2 input variables cooperate to control a cartpole system in terms of balancing as well as centering. The cooperation is due to the BOXES scheme that selects one of the fuzzy controllers for each time step according to the content of box that is established through the critic of the control action by the fuzzy controllers. It is found that the control scheme is good at controlling the cartpole system so that the system is stabilized fast while the BOXES develops its ability to select proper fuzzy controller through experience.

• Smart Structures and Systems
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• 제23권1호
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• pp.1-14
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• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• International Journal of Control, Automation, and Systems
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• 제5권4호
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• pp.444-455
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• 2007

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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권2호
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• pp.154-160
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• 2009

A control system composed of two fuzzy controllers is proposed to balance the pole as well as to move the cart to the center of the track of the cartpole system. The two fuzzy controllers are designed with 2 input variables respectively and their control characters are studied in order to devise a control scheme that alternates the two fuzzy controllers. It is found that the control system using the scheme works well even though there is some residual oscillations of the pole and the cart.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.249-254
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• 2001

Abstract In this paper, we propose a method of cooperative control based on artifical intelligent system in distributed autonomous robotic system. In general, multi-agent behavior algorithm is simple and effective for small number of robots. And multi-robot behavior control is a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. However when the number of robot goes on increasing, this becomes difficult to be realized because multi-robot behavior algorithm provide on multiple constraints and goals in mobile robot navigation problems. As the solution of above problem, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for obstacle avoidance. Here, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for their direction to avoid obstacle. Our focus is on system of cooperative autonomous robots in environment with obstacle. For simulation, we divide experiment into two method. One method is motor schema-based formation control in previous and the other method is proposed by this paper. Simulation results are given in an obstacle environment and in an dynamic environment.

• 한국지능시스템학회논문지
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• 제16권3호
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• pp.349-356
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• 2006

운반차-막대 시스템을 제어하기 위하여 두 개의 2 차원 퍼지 제어기가 CMAC에 의해 협동하게 하는 제어 계략을 개발하였다. 제어계에서 한 제어기는 운반차의 변위와 속도, 다른 제어기는 막대의 각도와 각속도를 각각의 2 개의 입력 변수로 하고 운반차에 가하는 힘이 두 제어기의 출력 변수인데, 이 변수를 외부의 감독에 따라 CMAC이 학습하게 하여 협동 제어의 효과를 발휘한다. 제어계 구성과 CMAC 훈련에 의한 협동 계략의 단순함에 비하여, 제어계는 4 개의 입력 변수에 의한 퍼지 제어기나 다른 해석적 방법에 의한 것에 비해 손색없는 제어 성능을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.468-472
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• 1992

In recent years, lots of researches on autonomous mobile robot have been accomplished. However they focused on environment recognition and its processing to make a decision on the motion, And cooperative multi-robot, which must be able to avoid crash and to make mutual communication, has not been studied much. This paper deals with cooperative motion of two robots, 'Meari 1" and "Meari 2 " made in our laboratory, based on communication between the two. Because there is an interference on communication occurring in cooperative motion of multi-robot, many restrictive conditions are required. Therefore, we have designed these robot system so that communication between them is available and mutual interference is precluded, and we used fuzzy interference to overcome unstability of sensor data.of sensor data.

• 한국지능시스템학회논문지
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• 제6권2호
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• pp.111-119
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• 1996

In this paper, we propose time-optimal trajectory planning algorithms for cooperative multi-robot manipulators system considering optimal load distribution. Internal forces essentially effect on time optimal trajectory planning and if they are comitted, the time optimal scheme is not no longer true. Therefore, we try to find the internal force factors of cooperative robot manipulators system in a time-optimal aspect. In this approach, a specific generalized inverse is used and is fuzzified for the purpose. In this optimal method, the fuzzy logic concept is used and selected for diminishing computation time, for finding the load distribution factors.

• 한국위성정보통신학회논문지
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• 제8권3호
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• pp.6-9
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• 2013

Cognitive Radio는 유휴 스펙트럼을 찾아 환경에 맞는 통신방식과 주파수 대역폭을 능동적으로 판단해 재활용하는 지능적인 간섭회피로 방식으로 스펙트럼을 공유하여 전파자원효율을 극대화 하는 기술이다. 이때 PU에 간섭을 일으키지 않기 위한 스펙트럼 센싱 기술이 중요하다. 하지만 상황에 따라 스펙트럼 센싱 알고리즘을 선택할수 있다면 좀더 효율적 센싱을 할수 있을 것이다. 따라서 본 논문에서는 퍼지컨트롤러를 통해 에너지검출, 자기상관검출,M atched filter 검출 등을 개별 센싱에 반영하는 시스템 모델을 제안하고 분석한다.

• Transactions on Control, Automation and Systems Engineering
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• 제1권2호
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• pp.147-152
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• 1999

It is an interesting area in the field of artifical intelligence to find an analytic model of cooperative structure for multiagent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent agents solving a pursuit problem in a continuous world. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to find the fuzzy logic controller seems to be promising.