• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.986-992
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• 2021

To stabilize closed-loop wireless control systems, the state-of-the-art approach receives the individual sensor measurements at the controller and then sends the computed control signal to the actuators. We propose an over-the-air controller scheme where all sensors attached to the plant transmit scaled sensing signals simultaneously to the actuator, and the actuator then computes the feedback control signal by scaling the received signal. The over-the-air controller essentially adopts the over-the-air computation concept to compute the control signal for closed-loop wireless control systems. In contrast to the state-of-the-art sensor-to-controller and controller-to-actuator communication approach, the over-the-air controller exploits the superposition properties of multiple-access wireless channels to complete the communication and computation of a large number of sensing signals in a single communication resource unit. Therefore, the proposed scheme can obtain significant benefits in terms of low actuation delay and low resource utilization with a simple network architecture that does not require a dedicated controller.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

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

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.753-763
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• 2015

This paper proposes a decentralized robust adaptive neural network control scheme using multiple radial basis function neural networks for electrically driven robot manipulators with bounded input voltages in the presence of uncertainties. The proposed controller considers both robot link dynamics and actuator dynamics. Practically, the controller gain coefficients applied at each joint may be nonlinear time-varying and the input voltage at each joint is saturated. The proposed robot controller overcomes the various uncertainties and the input voltage saturation problem. The proposed controller does not require any robot and actuator parameters. The adaptation laws of the proposed controller are derived by using the Lyapunov stability analysis and the stability of the closed-loop control system is guaranteed. The validity and robustness of the proposed control scheme are verified through simulation results.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.60-69
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• 2010

Lookup-Table (LUT) based fuzzy controller for obstacle avoidance enhances operations faster in multiple obstacles environment. An LUT based fuzzy controller with Positive/Negative (P/N) fuzzy rule base consisting of 18 rules was introduced in our paper$^1$ and this paper shows a 50-rule P/N fuzzy controller for enhancing performance in obstacle avoidance. As a rule, the more rules are necessary, the more buffers are required. This paper suggests LUT sharing method in order to reduce LUT buffer size without significant degradation of performance. The LUT sharing method makes buffer size independent of the whole fuzzy system's complexity. Simulation using MSRDS(MicroSoft Robotics Developer Studio) evaluates the proposed method, and in order to investigate its performance, experiments are carried out to Pioneer P3-DX in the LabVIEW environment. The simulation and experiments show little difference between the fully valued LUT-based method and the LUT sharing method in operation times. On the other hand, LUT sharing method reduced its buffer size by about 95% of full valued LUT-based design.

• Journal of Power Electronics
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• v.16 no.1
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• pp.205-216
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• 2016

The manufacturing sector resorts to automation to increase production and homogeneity of products during mass production, without increasing scarce, expensive, and unreliable manpower. Automation in the form of multiple robotic arms that handle materials in all directions in different stages of the process is proven to be the best way to increase production. This paper thoroughly investigates robotic single-arm movements, that is, 360° vertical rotation, with the help of a brushless DC motor, controlled by a fuzzy proportional-integral-derivative (PID) controller. This paper also deals with the design and performance of the fuzzy-based PID controller used to control vertical movement against the limited scope of conventional PID feedback controller and how the torque of the arm is affected by the fuzzy PID controller in the four quadrants to ensure constant speed and accident-free operation despite the influence of gravitational force. The design was simulated through MATLAB/SIMULINK and integrated with dSPACE DS1104-based hardware to verify the dynamic behaviors of the arm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1032-1040
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• 2012

This paper proposes controller of leader robot considering following robot with input constraints based on leader-following approach. In the previous formation control researches, it was assumed that leader and follower is same object. If leader robot drives as maximum speed that the initial position errors still remain even if following robot have same velocity as a leader. In the situation that velocity of following robot is lower than its leader robot, following robot cannot follow leader robot. Furthermore, the following robot will not be able to made formation with leader robot and keep proximity communication or sensing range. Therefore, multiple mobile robot system using leader-following method should be guaranteed range to get information each other. In this paper, Leader robot is driving to goal position using linear controller and following robot is following trajectory to be made from leader robot. We assume that following robot has input constraints to realize different performance between leader robot and following robot. We design controller of leader robot for desired goal position including the errors between formation and following robot. Thus, we propose leader robot controller considering input constraints of following robot. Finally, we were able to confirm the validity of the proposed method based on simulation results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.211-217
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• 1998

Many practical control problems for the complex, uncertain or large-scale plants, need to simultaneously achieve a number of objectives, which may conflict or compete with each other. If the conventional optimization methods are applied to solve these control problems, the solution process may be time-consuming and the resulting solution would ofter lose its original meaning of optimality. Nevertheless, the human operators usually performs satisfactory results based on their qualitative and heuristic knowledge. In this paper, we investigate the control strategies of the human operators, and propose a fuzzy model-based multi-objective satisfactory controller. We also apply it to the automatic train operation(ATO) system for the magnetically levitated vehicles(MAGLEV). One of the human operator's strategies is to predict the control result in order to find the meaningful solution. In this paper, Takagi-Sugeno fuzzy model is used to simulated the prediction procedure. Another str tegy is to evaluate the multiple objectives with respect to their own standards. To realize this strategy, we propose the concept of a satisfactory solution and a satisfactory control scheme. The MAGLEV train is a typical example of the uncertain, complex and large-scale plants. Moreover, the ATO system has to satisfy multiple objectives, such as seed pattern tracking, stop gap accuracy, safety and riding comfort. In this paper, the speed pattern tracking controller and the automatic stop controller of the ATO system is designed based on the proposed control scheme. The effectiveness of the ATO system based on the proposed scheme is shown by the experiments with a rotary test bed and a real MAGLEV train.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1958-1969
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• 1996

This paper proposed a new approach to adaptive call admission control based on a neural network for multiple service classes with different quality of service (QoS) in the ATM-based Broadband Integrated Services Digital Networks. the proposed method extend Hiramatsu's neural network based "leaky pattern table" method for the single QoS[1, 2, 3] to deal with multiple services with different QoS by constructing multiple pattern tables based on each service's acceptance or rejection at the call set-up requests, and by simultaneously controlling each service's QoS according to the target QoS of the service and the trunk capacity. Computer simulation results on two service classes with different traffic characteristics and different cell loss rates as QoS, highlight good performance and effectiveness of the proposed call admission controller for multiple service classes.e classes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.191-194
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• 2000

In this paper, an output tracking control technique of discrete-time Takagi-Sugeno (TS) fuzzy systems is developed. The TS fuzzy system is represented as an uncertain multiple linear system. The tracking problem of TS fuzzy system is converted into the stabilization problem of a uncertain multiple linear system. A sufficient condition for asymptotic tracking is obtained in terms of linear matrix inequalities (LMI). A design example is illustrated to show the effectiveness of the proposed method.