• 대한전기학회논문지
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• 제37권10호
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• pp.733-739
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• 1988

Adaptive controllers for linear system whose nominal values of coefficients only are known, that is corrupted by disturbance, are designed by signal synthesis model reference adaptive control (MRAC). This design is stemmed from the Lyapunov direct method. To reduce the model following error and to improve the conrergence rate of the design, an indirect suboptimal control law is de rived using the Hamilton Jacobi Beellman equation. Proper compensaton for the effects of time varying coefficients and plant disturbance are suggested. In the design procedure no complete identification of unknown coefficients are required.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.239-239
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• 2000

This paper present experimental results on temperature trajectory tracking. The benefits of precalculated feedforward input together with PID feedback control are demonstrated by experimental results. To find the feedforward input, the plant (autoregresiive) model is first identified and convex optimization procedure is applied. PID controller is then implemented based on Ziegler-Nickels tuning rule to reduce effects of disturbances and modeling errors. Experimental results show an improvement in slope tracking performance over the fully PID controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.974-976
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• 1996

In this paper, a reference model following control system using a fuzzy logic controller(FLC) is proposed By using an integrator and a nonlinear hysteresis element, a reference model whose response has no overshoot and fast rise time is designed. A FLC is designed to follow as close as possible to the response of the reference model. The proposed design method is shown that the robustness and the optimal tracking property can be achieved under modeling error, disturbance and parameter perturbations. The effectiveness of the proposed design method is verified through the simulation that compare using the FLC with using a $H_{\infty}$ controller.

• Journal of Information Processing Systems
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• 제18권6호
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• pp.755-762
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• 2022

The existing electrical engineering design method and the dynamic objective function in the application process of automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design method based on neural network and the application of automatic control system. This paper analyzes the structure and working mechanism of electrical engineering automation control system by an automation control model with main control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerant control is figured out. The automatic control power coefficient is distributed based on an ideal control effect of system. According to the distribution results, an automatic control algorithm is based on neural network for accurate control. The experimental results show that the electrical automation control method based on neural network can significantly reduce the control following error to 3.62%, improve the accuracy of the electrical automation tracking control, thus meeting the actual production needs of electrical engineering automation control system.

• 한국멀티미디어학회논문지
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• 제17권7호
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• pp.787-796
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• 2014

Many mobile robot navigation methods utilize laser scanners, ultrasonic sensors, vision camera, and so on for detecting obstacles and path following. However, human utilizes only vision(e.g. eye) information for navigation. In this paper, we study a mobile robot control method based on only the camera vision. The Gaussian Mixture Model and a shadow removal technology are used to divide the foreground and the background from the camera image. The mobile robot uses a combined CAMSHIFT and KLT feature tracker algorithms based on the information of the foreground to follow a person. The algorithm is verified by experiments where a person is tracked and followed by a robot in a hallway.

• 자동차안전학회지
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• 제11권3호
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1986년도 하계학술대회논문집
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• pp.145-148
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• 1986

• 한국정밀공학회지
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• 제14권5호
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• pp.92-99
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• 1997

A model reference adaptive control(MRAC) theory is very useful for controlling a plant of which the parameters are unknown or vary during operation usint only input-output signal of plant. In this study, 2' nd order discreter time MRAC controller is designed for an electrohydraulic position control system which is represented with nonlinear mathematical model and the least square method is adopted for the para-meter adjustment law. This control algorthm is applied to the position control of electrohydraulic servosystem through computer simulation and the effect of the change of load, sampling time upon the performance following reference model and upon the performance of estimating plant parameters are examined.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• 전기학회논문지
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• 제60권8호
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• pp.1592-1598
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• 2011

In this paper, a formation control method based on the leader-following approach for nonholonomic mobile robots is proposed. In the previous works, it is assumed that the followers know the leader's velocity by means of communication. However, it is difficult that the followers correctly know the leader's velocity due to the contamination or delay of information. Thus, in this paper, an adaptive approach based on the parameter projection algorithm is proposed to estimate the leader's velocity. Moreover, the adaptive backstepping technique is used to compensate the effects of a dynamic model with the unknown time-invariant and time-varying parameters. From the Lyapunov stability theory, it is proved that the errors of the closed-loop system are uniformly ultimately bounded. Simulation results illustrate the effectiveness of the proposed control method.