• Smart Structures and Systems
• /
• v.30 no.1
• /
• pp.1-15
• /
• 2022

This article focuses on stability analysis and fuzzy controller synthesis for large neural network (NN) systems consisting of several interconnected subsystems represented by the NN model. Advanced and fuzzy NN differential inclusion (NNDI) for stability based on the developed algorithm with H infinity can be designed based on the evolved biological design. This representation is constructed using sector linearity for NN models. Sector linearity transforms a non-linear model into a linear model based on proposed operations. New sufficient conditions are realized in the form of LMI (linear matrix inequalities) to ensure the asymptotic stability of the trans-Lyapunov function. This transforms the nonlinear model into a linear model based on multiple rules. At last, a numerical case study with simulations is derived as illustration to prove its feasibility in real nonlinear structures.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.1
• /
• pp.91-96
• /
• 2003

The forces and moments at the aircraft c.g. have components due to aerodynamic effects and to engine thrust. For the flight stability and autopilot systems we present a attitude control method using an intelligent control algorithm Which is based on the control rules from experts knowledge concerning the motion equations and other experiences. Then a robust fuzzy controller is developed to control the flight attitude. The controller can deal with multiple inputs and outputs. We have made an aircraft model and the orientation sensor for experimental flights. The control rules based on the flight expert s experience and knowledge can be programmed by fuzzy rules, and determined control rules by experimental flight. We can be stable attitude control by fuzzy controller.

• The Journal of Korea Robotics Society
• /
• v.2 no.3
• /
• pp.212-220
• /
• 2007

This paper proposes a method of avoiding obstacles and tracking a moving object continuously and simultaneously by using new concepts of virtual tow point and fuzzy danger factor for differential wheeled mobile robots. Since differential wheeled mobile robot has smaller degree of freedom to control and are non-holonomic systems, there exist multiple solutions (trajectories) to control and reach a target position. The paper proposes 'fuzzy danger factor' for obstacles avoidance, 'virtual tow point' to solve non-holonomic object tracking control problem for unique solution and three kinds of fuzzy logic controller. The fuzzy logic controller is policy decision controller with fuzzy danger factor to decide which controller's result is more valuable when the mobile robot is tracking a moving object with obstacles to be avoided.

• Journal of Korean Institute of Industrial Engineers
• /
• v.23 no.4
• /
• pp.803-816
• /
• 1997

In this paper we propose a new approach far part families considering multiple attributes of parts in the design and operating stage of flexible manufacturing systems. We first represent the relationship of parts and the relative attributes using fuzzy membership function, AHP method and normalization. As a result, more realistic nonbinary data of the relationship is obtained. Then we group parts into part families based on the nonbinary data using fuzzy $\alpha$-cut and new similarity coefficient method. The performance of our method is compared numerically with others.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.6
• /
• pp.549-553
• /
• 2002

This paper develops knowledge-based multiple fuzzy rules for researcher-selection by automatic ranking process. Inference rules for researcher-selection are created, then the multiple fuzzy rule system with max-min inference is applied. The way to handle for selection standards according to a certain criteria in dynamic manner, is also suggested in a simulation model. The model offers automatic, fair, and trust decision for researcher-selection processing.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.7
• /
• pp.635-641
• /
• 2008

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.

• Journal of Institute of Control, Robotics and Systems
• /
• v.1 no.1
• /
• pp.50-57
• /
• 1995

In this paper, the design and the implementation of the controller for an articulate robot, which is developed in our Automatic Control Laboratory, are mainly discussed. The controller reduces software computational load via distributed processing method using multiple CPU's, and simplifies structures by the time-division control with TMS320C31 DSP chip. The method of control is based on the fuzzy-compensated PID control with scale factor, which compensates for the influence of load variation resulting from the various postures of the robot with conventional PID scheme. The application of the proposed controller to the robot system with DC servo-motors shows some excellent control capabilities. Also, the response characteristics of system for the various trajectory commands verify the superiority of the controller.

• Journal of Industrial Technology
• /
• v.17
• /
• pp.323-330
• /
• 1997

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure, and the mountain clustering technique is used in partitioning of system. TSK rule structure is adopted to form the fuzzy rules, and Back propagation algorithm is used for learning parameters in consequent parts of the rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators. Computer simulations are performed to verify the effectiveness of the proposed method.

• Industrial Engineering and Management Systems
• /
• v.4 no.2
• /
• pp.218-227
• /
• 2005

In this research a model for establishing a new, rational fee schedule for general surgical procedures in a national health insurance program is developed. A fuzzy multiple criteria decision-making (FMCDM) model is proposed. The relative values of eleven surgical procedures were obtained through an empirical study based on the FMCDM model. Consequently, a new fee schedule obtained from the FMCDM model. This new fee schedule is more convincing than previous schedule and more persuasive to the references for the policy setting.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.258-258
• /
• 2000

In this paper, we present the position control of pendulum system which is driven by a ultrasonic motor. Since the system's response is different for each initial position of pendulum, it is difficult to obtain the satisfiable control performance by using a neural network which is learned by off-line. To overcome this problem, we propose the multiple controller based on fuzzy inference system for ultrasonic motor. and controller is designed by neural network. The proposed method shows good performance for any initial positions and it's effectiveness is verified from experiments. We expect that ultrasonic motor can be used as actuators of robot's leg or manipulator.