• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.308-313
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• 2005

For a real-time quadruped robot running control, there are many important objectives to consider. In this paper, the running control architecture based on global states, which describe the cyclic target motion, and central pattern is proposed. The main goal of the controller is how the robot can have robustness to an unpredictable environment with reducing calculation burden to generate control inputs. Additional goal is construction of a single framework controller to avoid discontinuities during transition between multi-framework controllers and of a training-free controller. The global state dependent neuron network induces adaptation ability to an environment and makes the training-free controller. The central pattern based approach makes the controller have a single framework, and calculation burden is resolved by extracting dynamic equations from the control loop. In our approach, the model of the quadruped robot is designed using anatomical information of a cat, and simulated in 3D dynamic environment. The simulation results show the proposed single framework controller is robustly performed in an unpredictable sloped terrain without training.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.29-35
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• 2016

In this paper, we propose a scheme to prolong the lifetime of the sensor network by reducing the power consumption of the sensor node. The proposed algorithm reduces the number of transmissions and sensing at the application layer. We combine the VIS scheme with the MSZ algorithm and call it as the SZM/VIS algorithm. The actual temperature data was collected using the sensor nodes to assess the performance of the proposed algorithm. The proposed algorithm was implemented through the programming and was evaluated under various setting values. Experimental results show that the SZM/VIS has a slightly improved transmission ratio than that of the MSZ while has the periodic transmission capability like as the MSZ. Also the SZM/VIS can significantly reduces the sensing ratio like that of the VIS. Our algorithm has the advantages of instantaneous, simplicity, small overhead and robustness. Our algorithm has just negligible side effects by controlling the parameter properly depending on the application types. The SZM/VIS algorithm will be able to be used effectively for the applications that need to be managed within a certain range of specific properties, such like crop management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1558-1564
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• 2006

This study addressed security requirements for ad-hoc network environments, which lies at the hour of the ubiquitous computing revolution and proposed a partially-distributed certificate management system that can ensure security in mobile ad-hoc networks. The proposed model is characterized by ie ability to handle dynamic mobility of nodes, minimize routing load and enhance expandability of network by allowing participating nodes to authenticate each other without being interrupted by joining the cluster. The security, efficiency and robustness of the proposed model were evaluated through simulation.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.242-244
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• 1992

Conventionally in the industrial control, PlD controller has been used because of its robustness, and nonlinear characteristic of a system under control. Although the PlD controller produce suitable parameter of the each system and also variable of PlD controller should be changed according to environment, disturbance, load. In this paper, the convergence and learning accuracy of the back-propagation(BP) method in neural network are investigated by analyzing the reason for decelerating the convergence of BP method. and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative. The modified logistic activation function it proposed by defining the convergence factor based on the analysis and applied to the position and speed control of a DC-servo motor. This paper revealed for experimental, a neural network and a PD controller combined off-line system using developed the position and speed characteristics of a DC-servo motor.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.319-328
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• 2010

This paper proposes a robust adaptive fuzzy tracking control scheme for a nonholonomic mobile robot with external disturbances as well as parameter uncertainties in the robot kinematics, the robot dynamics, and the actuator dynamics. In modeling a mobile robot, the actuator dynamics is integrated with the robot kinematics and dynamics so that the actuator input voltages are the control inputs. The presented controller is designed based on a FBFN (Fuzzy Basis Function Network) to approximate an unknown nonlinear dynamic function with the uncertainties, and a robust adaptive input to overcome the uncertainties. When the controller is designed, the different parameters for two actuator models in the actuator dynamics are taken into account. The proposed control scheme does not require the kinematic and dynamic parameters of the robot and actuators accurately. It can also alleviate the input chattering and overcome the unknown friction force. The stability of the closed-loop control system including the kinematic control system is guaranteed by using the Lyapunov stability theory and the presented adaptive laws. The validity and robustness of the proposed control scheme are shown through a computer simulation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.27-35
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• 1998

In this paper we will develope the immunized recurrent neural network control system with high robustness in dynamically changing environmental conditions. The variation of internal parameters of a system and external(or internal) disturbances can be considered as antigen, and the control input which can be regarded as antibody can be generated against uncertainties. The antibody will be generated from previous control informations and if a antibody for an antigen can not be generated from the corresponding information. the immune system produces another antibody by genetic operations. We apply this concept to a robot manipulator and evaluate the effectiveness of the above proposed system.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.505-519
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• 2005

In this paper, we develop an anti-sway control in proposed techniques for an ATC system. The developed algorithm is to build the optimal path of container motion and to calculate an anti-collision path for collision avoidance in its movement to the finial coordinate. Moreover, in order to show the effectiveness in this research, we compared NNP PID controller to be tuning parameters of controller using NN with 2 DOF PID controller. The simulation and experimental results show that the proposed control scheme guarantees performances, trolley position, sway angle and settling time in NNP PID controller than other controller. As the results in this paper, the application of NNP PID controller is analyzed to have robustness about disturbance which is wind of fixed pattern in the yard. Accordingly, the proposed algorithm in this study can be readily used for industrial applications.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.3
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• pp.207-213
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• 2005

This paper deals with an autonomous flight guidance and control algorithm design for TR301 tilt-rotor airplane under development by Korea Aerospace Research Institute for simulation purpose. The objective of this study is to design autonomous flight algorithm in which the tilt-rotor airplane should follow the given waypoints precisely. The approach to this objective in this study is that, first of all, model-based inversion is applied to the highly nonlinear tilt-rotor dynamics, where the tilt-rotor airplane is assumed to fly at helicopter flight mode(nacelle angle=0 deg), and then the control algorithm, based on classical control, is designed to satisfy overall system stabilization and precise waypoint following performance. Especially, model uncertainties due to the tiltrotor model itself and inversion process are adaptively compensated in a simple neural network(Sigma-Phi NN) for performance robustness. The designed algorithm is evaluated in the tilt-rotor nonlinear airplane in helicopter flight mode to analyze the following performance for given waypoints. The simulation results show that the waypoint following responses for this algorithm are satisfactory, and control input responses are within control limits without saturation.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1140-1141
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• 2008

The paper proposes a novel control algorithm for tracking maximum power of PV generation system. The maximum power of PV array is determinated by a insolation and temperature. Prior considered the term in PV generation system is how maximum power point is accurately tracked. The paper proposes a Fuzzy-Neuro control algorithm so as to accurately track those maximum power points. The proposed control algorithm comprises the antecedence part of fuzzy rule and clustering method, multi-layer neural network in the consequent part. Fuzzy-Neuro has the advantages which are depicted both high performance and robustness in Fuzzy control and high adaptive control in Neural Network. Specially, it can show the outstanding control performance for parameter variations appling to non-linear character of PV array. In paper, the tracking speed and the accuracy prove the validity through comparing a proposed algorithm with a conventional one.