• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.283-292
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• 1996

Automatic guidance of farm tractors would improve productivity by reducing operator fatigue and increasing machine performance. To control tractors within $\pm$5cm of the desired path, fuzzy and adaptive steering controllers were developed to evaluate their characteristics and performance. Two input variables were position and yaw errors, and a steering command was fed to tractor model as controller output. Trapezoidal membership functions were used in the fuzzy controller, and a minimum-variance adaptive controller was implemented into the 2-DOF discrete-time input-output model. For unit-step and composite paths, a dynamic tractor simulator was used to test the controllers developed. The results showed that both controllers could control the tractor within $\pm$5cm error from the defined path and the position error of tractor by fuzzy controller was the bigger of the two. Through simulations, the output of self-tuning adaptive controller was relatively smooth, but the fuzzy controller was very sensitive by the change of gain and the shape of membership functions. Contrarily, modeling procedure of the fuzzy controller was simple, but the adaptive controller had very complex procedure of design and showed that control performance was affected greatly by the order of its model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.8
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• pp.1939-1951
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• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.233-240
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• 2006

This paper presents the robust control method using a self recurrent wavelet neural network (SRWNN) via adaptive backstepping design technique for stable walking of biped robots with unknown model uncertainties. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the biped robots. The adaptation laws for weights of the SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Computer simulations of a five-link biped robot with unknown model uncertainties verify the validity of the proposed control system.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.209-214
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• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• Smart Structures and Systems
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• v.17 no.4
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• pp.611-629
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• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.9
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• pp.655-662
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• 1988

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a deired trajectory in spite of the presence of nonlinearies and parameter uncertainties in robot dynamic models. In this paper, an adaptive control scheme for a robot manipulator is proposed to design the self-tuning controller which controls the extended linearized perturbaton model via the pole placement, and this control. The feasibility of the controller is demonstrated by the simulation about position control of a three-link manipulator with payload and parameter uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.584-588
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• 1991

This paper presents a fuzzy adaptive controller which can improve the control policy automatically. Adaptation is achieved by the addition of on-line identification of the fuzzy inverse model using input-output data pairs of the process. Starting with an initial crude control rule, the adaptive controller matches the model to the process to self-tune the controller. The control algorithm needs much less memory of computer than other SOC algorithms.

• Korean Journal of Human Ecology
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• v.18 no.6
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• pp.1209-1219
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• 2009

The purpose of this study examined the path model of maternal emotional expression, temperament and self-esteem on emotion regulation among children. The subjects were 487 5th and 6th graders. Data was gathered through questionnaires reported by children and their mothers and analyzed by structural equation modeling. The results showed that children's 'activity level' temperament and maternal negative emotional expression directly affected maladaptive emotion regulation. Children's 'emotionality' temperament and maternal positive emotional expression directly affected adaptive emotion regulation. Children's 'approach-flexibility' temperament and self-esteem directly affected both maladaptive and adaptive emotion regulation. Maternal emotional expression and children's self-esteem mediated between children's temperament and emotion regulation. Additionally, the most important variable predicting children's maladaptive emotion regulation was the children's 'activity level' temperament, and the most important variable for adaptive emotion regulation was the children's 'emotionality' temperament.

• Journal of Information Technology Applications and Management
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• v.18 no.1
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• pp.15-39
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• 2011

A deeper understanding of how organizations behave as social complex adaptive systems is needed. In this paper we demonstrate how the Leadership Invigorating Flows of Energies model can help with this understanding. The model highlights the role of emergent leadership as a force encouraging the creation, diffusion, and utilization of knowledge through self-organizing mechanisms. We illustrate our approach by examining Wikipedia and show how it can be described as a social CAS. Our analysis of Wikipedia describes how emerging intrapreneurship behaviors result in dynamic flows of knowledge and self-organizing feedback mechanisms across the organization. We provide implications for organization studies and present evidence to support claims made by advocates of complexity theory. We conclude by proposing that Wikipedia can be seen as a new form of organization, and finish with a brief note highlighting a possible way forward.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.355-359
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• 2006

For the poor observability of azimuth misalignment angle and east gyro drift rate of the traditional initial alignment, a bran-new SINS stationary fast self-alignment approach is proposed. By means of analyzing the characteristic of the strapdown inertial navigation system (SINS) stationary alignment seriously, the new approach takes full advantage of the specific force and angular velocity information given by inertial measurement unit (IMU) instead of the mechanization of SINS. Firstly, coarse alignment algorithm is presented. Secondly, a new fine alignment model for SINS stationary self-alignment is derived, and the observability of the model is analysed. Then, a modified Sage-Husa adaptive Kalman filter is introduced to estimate the misalignment angles. Finally, some computer simulation results illustrate the efficiency of the new approach and its advantages, such as higher alignment accuracy, shorter alignment time, more self-contained and less calculation.