• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.58-61
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• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.927-934
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• 2017

This paper is concerned with applicability of two-degree-of-freedom controllers to the recently suggested i-PID controllers, in which unknown parts of the plant are taken into account without any modeling procedure. First, i-PID controller with two-degree-of-freedom is applied to a specific model, called Anisochronic model, to confirm the usefulness of this method. Second, using the original examples of i-PID controllers, it is confirmed that performance/robustness of system are to be improved due to two-degree-of-freedom, especially when the input changes suddenly. It is seen that as the desired robustness increases the optimal value of two-degree-of-freedom parameter ${\alpha}_A$ would be negative. It is checked and verified that if this value was limited to 1 or less as is generally known, performance would be degraded.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2292-2295
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• 2001

In this paper, The control problem for a series DC motor is considered to adaptive fuzzy sliding-mode control scheme. Based on a nonlinear mathematical model of a series connected DC motor, instead of the combination of a nonlinear transformation and state feedback(feedback linearization) reduces the nonlinear control design. To demonstrate its effectiveness, an experimental study of this controller is presented. Two sets of fuzzy rule bases are utilized to represent the equivalent control input with unknown system functions of the main target. The membership functions of the THEN-part, which is used to construct a suitable equivalent control of SMC, are changed according to the adaptive law. With such a design scheme, we not only maintain the distribution of membership functions over state space but also reduce computing time considerably.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.282-284
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• 1995

A robust nonlinear control technique for brushless DC(BLDC) motors is presented using a feedback linearizing technique. The nonlinear model of the BLDC motor is first linearized far the exactly known system by an input-output linearizing method. Then, the robust control is designed for the unknown parts of the system using the Lypunov second method. By employing the proposed control scheme, the a robust control performance against the parameter uncertainties is obtained and therefore a robust feedback linerizing control of the BLDC motor is realized. The effectiveness of the proposed control scheme is well demonstrated through the comparative simulations.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.1010-1013
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• 2003

This paper describes an algorithm for determining robot location using appearance-based paradigm. This algorithm compress the image set using PCA(principal component analysis) to obtain a low-dimensional subspace, called the eigenspace, and it makes a manifold that represent a continuous-appearance function. To determine robot location, given an unknown input image, the recognition system first projects the image to eigenspace. Neural network use coefficients of the eigenspace to estimate the location of the mobile robot. The algorithm has been implemented and tested on a mobile robot system. In several trials it computes location accurately.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.138-144
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• 2000

Robot manipulators, which are nonlinear structures and have uncertain system parameters, have complex in dynamics when are operated in unknown environment. To compensate for estimate errors of the uncertain system parameters and to accomplish the desired trajectory tracking, nonlinear robust controllers are appropriate. However, when estimation errors or tracking errors are large, they require large input torques, which may not be satisfied due to torque limits of actuators. As a result, their stability can not be guaranteed. In this paper, a new robust control scheme is presented to solve stability problem and to achieve fast trajectory tracking in the presence of torque limits. By using fuzzy logic, new desired trajectories which can be reduced are generated based on the initial desired trajectory, and torques of the robust controller are regulated to not exceed torque limits. Numerical examples are shown to validate the proposed controller using an uncertain two degree-of-freedom underwater robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.34.4-34
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• 2001

It is well known that one can easily design a high-gain adaptive output feedback control for a class of nonlinear systems which satisfy a certain condition so called output feedback exponential passivity (OFEP). The designed high gain adaptive controller has simple structure and high robustness with regard to bounded disterbances and unknown order of the controlled system. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper, we deal with a design problem of the robust high-gain adaptive output feedback control for the OFEP nonlinear systems with uncertain nonlinearities and/or disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.88.4-88
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• 2001

In this paper, we propose a solver for differential equations, using a multi-layer neural network. The multi-layer neural network is a transformer function originally where the function is differential and the explicit representation has been developed. The learning determines the response of neural networks; however, the response is not equal to the output values. The differential relations are also the response. The differential conditions can be also set as teaching data; therefore, there is a possibility to reach a new solver for the differential equations. Since it is unknown how to define the input data for the neural network solver during long terms, we could not derive the expressions. Recently, the analogue type neural network is known and it transforms any vector to another The "any" must be...

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.35-38
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• 2008

This paper develops an aero engine LPV mathematical model to exactly describe aero engine dynamic process characteristics, eliminate the effect of modeling error. Design FDF with eigenstructure assignment. The simulation results of turbofan engine control system sensor fault show that this method has good performance in focusing discrimination in fault signal with modeling eror, enhancing the robustness to unknown input, detecting accuracy is high and satisfiying real-time requirement.

• Earthquakes and Structures
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• v.22 no.3
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• pp.289-304
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• 2022

During strong ground motions, adjacent structures with insufficient separation distances collide with each other causing considerable architectural and structural damage or collapse of the whole structure. Generally, existing design procedures for determining the separation distance between adjacent buildings subjected to structural pounding are based on approximations of the buildings' peak relative displacement. These procedures are based on unknown safety levels. This paper attempts to evaluate the influence of foundation flexibility on the structural seismic response by considering the variability in the system and uncertainties in the ground motion characteristics through comprehensive numerical simulations. Actually, the aim of this study is to evaluate the influence of foundation flexibility on probabilistic evaluation of structural pounding. A Hertz-damp pounding force model has been considered in order to effectively capture impact forces during collisions. In total, 5.25 million time-history analyses were performed over the adopted models using an ensemble of 25 ground motions as seismic input within OpenSees software. The results of the study indicate that the soil-structure interaction significantly influences the pounding-involved responses of adjacent structures during earthquakes and generally increases the pounding probability.