• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.195-202
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• 2020

The control system for the efficiency of resource utilization in sensor network environment based on object detection and environmental sensor requires active control function which based on sensor data acquisition and transmission functions and server's data analysis. Using active rule-based mobile agent middleware, this paper proposes a new distributed control framework that reduces the load of central sensor data server in sensor network environment by implementing remote data sensing and Zigbee-based communication with server and data analysis method of server. In addition, we implemented a power-saving system prototype using active rule-based distributed control methods that applied consumer's demand and environmental variables, and verified the validity of the proposed system through experiments and evaluations in the mobile agent middleware environment. The proposed system is a system framework that can efficiently autonomously control distributed objects in the sensor network environment, and it can be applied effectively to the development of demand response service based on optimal power control for the smart power system in the future.

• Journal of Navigation and Port Research
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• v.29 no.6 s.102
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• pp.555-560
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• 2005

Recently, many control systems are replaced with digital control systems in an effort to optimize the overall performance. In order to operate these systems efficiently, the conventional point-to-point connection method must be changed to the signal exchange via a communication network. This paper investigates the technical feasibility of the crane system using CAN protocol which is a part NMEA 2000 by implementing a network-based control system emulating the crane control system.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.515-525
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• 2007

In this paper, we develop a co-design methodology of stochastic optimal controllers and network parameters that optimizes the overall quality of control (QoC) in networked control systems (NCSs). A new dynamic model for NCSs is provided. The relationship between the system stability and performance and the sampling frequency is investigated, and the analysis of co-design of control and network parameters is presented to determine the working range of the sampling frequency in an NCS. This optimal sampling frequency range is derived based on the system dynamics and the network characteristics such as data rate, time-delay upper bound, data-packet size, and device processing time. With the optimal sampling frequency, stochastic optimal controllers are designed to improve the overall QoC in an NCS. This co-design methodology is a useful rule of thumb to choose the network and control parameters for NCS implementation. The feasibility and effectiveness of this co-design methodology is verified experimentally by our NCS test bed, a ball magnetic-levitation (maglev) system.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.12-18
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• 2008

This paper introduces the existing network-adaptive transport techniques for haptic-enhanced system. First we classify haptic-based network systems according to the communication architecture and data type. Then the existing studies concerning network QoS requirements for haptic-based network system are depicted. Finally, the survey of network-adaptive transport schemes is introduced devided into three key issues: delay and jitter compensation, error control, and transmission control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.

• Journal of Power Electronics
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• v.11 no.3
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• pp.279-284
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• 2011

Network-based controls for ac motor drive systems are becoming increasingly important. In this paper, an ac motor control system is implemented by a motor control module and three sensor modules such as a voltage sensor module, a current sensor module, and an encoder module. There will inevitably be network time delays from the sensor modules to the motor control system, which often degrades and even destabilizes the motor drive system. As a result, it becomes very difficult to estimate the network delayed ac sensor data. An algorithm to reduce the effects of network time delays on sensor data is proposed, using both a synchronization signal and a simple method for estimating the sensor data. The algorithm is applied to a vector controlled induction motor drive system, and the performance of the proposed algorithm is verified with experiments.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.139-148
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• 2024

In this paper, we propose a two-degree-of-freedom snake robot head system and an I-PID (Intelligent Proportional-Integral-Derivative)-based controller utilizing RBF (Radial Basis Function) neural network and adaptive robust terms as a control strategy to reduce rotation occurring in the snake robot head. This study proposes a two-degree-of-freedom snake robot head system to avoid complex snake robot dynamics. This system has a control system independent of the snake robot. Subsequently, it utilizes an I-PID controller to implement a control system that can effectively manage rotation at the snake robot head, the robot's nonlinearity, and disturbances. To compensate for the time delay estimation errors occurring in the I-PID control system, an RBF neural network is integrated. Additionally, an adaptive robust term is designed and integrated into the control system to enhance robustness and generate control inputs responsive to signal changes. The proposed controller satisfies stability according to Lyapunov's theory. The proposed control strategy was tested using a 9-degreeof-freedom snake robot. It demonstrates the capability to reduce rotation in Lateral undulation, Rectilinear, and Sidewinding locomotion.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.566-571
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• 1994

This paper presents in image-based visual servo control scheme for tracking a workpiece with a hand-eye coordinated robotic system using the fuzzy-neural-network. The goal is to control the relative position and orientation between the end-effector and a moving workpiece using a single camera mounted on the end-effector of robot manipulator. We developed a fuzzy-neural-network that consists of a network-model fuzzy system and supervised learning rules. Fuzzy-neural-network is applied to approximate the nonlinear mapping which transforms the features and theire change into the desired camera motion. In addition a control strategy for real-time relative motion control based on this approximation is presented. Computer simulation results are illustrated to show the effectiveness of the fuzzy-neural-network method for visual servoing of robot manipulator.

• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.4
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• pp.221-226
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• 2007

This paper propose a control network system based on IEEE1394 for a multi body robot control. The IEEE1394 has the characteristic of high speed(400Mbps), real-time, stability and plug&play. And IEEE1394 also supports freeform daisy chaining, branching and hot plugging, which reduce cabling complexity and make a system simple. Especially, multi host and broad casting support network data sharing method which is suitable for control network for multi body robot. Through experiment, we show that the proposed control network can interface 48 joints (BLDC motors, gears, and encoders) and four 6-axis force/torque sensors with 4Khz communication bandwidth, which is adequate for a multi body robot.