• Journal of Electrical Engineering and information Science
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• 제3권2호
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• pp.193-201
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• 1998

In this paper, the standard Dole, Glover, Khargoneker, and Francis (abbr. : DGKF 1989) H\ulcorner controller (H\ulcornerC) is extended to the nonlinear feedback linearization-H\ulcorner/sliding mode controller (NFL-H\ulcorner/SMC), to tackle the problem of the unmeasurable state variables as in the conventional SMC, to obtain smooth control as the linearized controller in a linear system, and to improve the time-domain performance under a worst scenario. The proposed controller is obtained by combining the H\ulcorner estimator with the nonlinear feedback linearization-sliding mode controller (NFL-SMC) and it does not need to measure all the state variables as in the traditional SMC. The proposed controller is applied as a nonlinear power system stabilizer (PSS) for the improvement of the power system damping characteristics of an single machine infinite bus system (SMIBS) connected through a double circuit line. The effectiveness of the proposed controller is verified by nonlinear time-domain simulation in case of a 3-cycle line-to-ground fault and in case of the parameter variations for the AVR gain K\ulcorner and for the inertia moment M.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2308-2310
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• 2001

In the adaptive fuzzy sliding mode control, from a set of a fuzzy IF-THEN rules adaptive fuzzy sliding mode control whose parameters are adjusted on-line according to some adaptation laws is constructed for the purpose of controlling the plant to track a desired trajectory. Most of the research works in nonlinear controller design using fuzzy systems consider the affine system with fixed grid-rule structure based on system state availability. The fixed grid-rule structure makes the order of the controller big unnecessarily, hence the on-line fuzzy rule structure and fuzzy observer based adaptive fuzzy sliding mode controller is proposed to solve system state availability problems. Therefore, adaptive laws of fuzzy parameters for state observer and fuzzy rule structure are established implying whole system stability in the sense of Lyapunov.

• 전자공학회논문지SC
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• 제42권6호
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• pp.1-8
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• 2005

슬라이딩 모드 제어기의 성능을 향상시키기 위해서 새로운 프로세스 모델의 파라미터 계산을 제안하였다. Camacho가 제안한 방법은 시간 지연항을 1차 Taylor 급수로 근사화하는 과정에서 발생되는 근사 오차에 의한 오버슈트, 정착시간 등에 문제점이 발생하였다. 본 논문에서는 이러한 문제점을 해결하기 위해 가중치를 고려한 변형된 파데 근사 방법과 이를 토대로 한 새로운 슬라이딩 모드 제어기의 동조 방법을 제안하였다. 제안된 방법은 프로세스 응답을 복잡한 수치적 과정없이 온라인으로 직접 동조 파라미터를 구할 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.614-619
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• 2004

The line-of-sight (LOS) stabilization system is a precision electro-mechanical gimbals assembly for rejecting vibration to isolate the load from its environment and point toward the target in a desired direction. This paper describes the design of gimbals system to reject the disturbance and to improve stabilization. To generate movement commands for the actuators in the stabilization system, the control system uses a sensor of angular rotation. The controller is a DSP with transducer and actuator interfaces. Unknown parameters of the gimbals are estimated using the signal compression method. The cross-correlation coefficient between the impulse response from the assumed model and the one from model of the gimbals is used to obtain the better estimation. And SMCPE (sliding mode control with perturbation estimation) is used to control the gimbals. SMCPE provides robustness of the control against the modeling deficiencies and unknown disturbances. In order to compare the performance of SMCPE with the classical SMC, a sample test result is presented.

• 전자공학회논문지SC
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• 제38권6호
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• pp.28-36
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• 2001

피드-포워드 보상부분과 불연속 제어 부분으로 구성되는 강건한 적응 슬라이딩 모드 로봇제어 알고리즘을 유도하였다. 미지의 매개변수는 실시간으로 추정되는 매개변수를 포함하는 그룹과 실시간으로 추정하지 않는 매개변수를 포함하는 그룹으로 나누어진다. 그런 다음 외란 및 실시간으로 추정하지 않는 매개변수에서의 불확실성 효과를 보상하기 위해 슬라이딩 제어 항이 토크 입력에 포함된다. 또한 매니퓨레이터 동역학 구조의 효율적인 이용으로 인하여 알고리즘은 계산이 간단하다. 매개변수 불확실성과 외부 외란의 존재에도 불구하고 제어기는 대국적 점근적으로 안정하며 추적오차가 영에 수렴함을 보여준다.

• International Journal of Aeronautical and Space Sciences
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• 제18권4호
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• pp.697-708
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• 2017

The complex battlefield environment makes it difficult to intercept maneuvering targets for guided missiles. In this paper, a finite-time convergent (FTC) guidance law based on the second-order sliding mode (SOSM) control theory is proposed to achieve the requirements of stability, accuracy and robustness. More specifically, a second-order sliding mode observer (SMOB) is used to estimate and compensate for the total disturbance of the controlled system, while the target acceleration is extracted from the line-of-sight (LOS) angle measurement. The proposed guidance law can drive the LOS angular rate converge to zero in a finite time, which means that the missile will accurately intercept the target. Numerical simulations with some comparisons are performed to demonstrate the superiority of the proposed guidance law.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.117-123
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• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• 한국해양공학회지
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• 제18권2호
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• pp.33-38
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• 2004

All adaptive neural network controller has been developed for a model of an underwater vehicle. This controller combines a radial basis neural network and sliding mode control techniques. No prior off-line training phase is required, and this scheme exploits the advantages of both neural network control and sliding mode control. An on-line stable adaptive law is derived using Lyapunov theory. The number of neurons and the width of Gaussian function should be chosen carefully. Performance of the controller is demonstrated through computer simulation.

• 제어로봇시스템학회논문지
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• 제13권10호
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• pp.990-999
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• 2007

We deal with second order NMP(Non-Minimum Phase) systems which are difficult to control with conventional methods because of their inherent characteristics of undershoot. In such systems, reducing the undesirable undershoot phenomenon makes the response time of the systems much longer. Moreover, it is impossible to control the magnitude of undershoot in a direct way and to predict the response time. In this paper, we propose a novel two sliding mode control scheme which is capable of determining the magnitude of undershoot and thus the response time of NMP systems a priori. To do this, we introduce two sliding lines which are in charge of control in turn. One is used to stabilize the system and achieve asymptotic regulation eventually like the conventional sliding mode methods and the other to stably control the magnitude of undershoot from the beginning of control until the state meets the first sliding line. This control scheme will be proved to have an asymptotic regulation property. The computer simulation shows that the proposed control scheme is very effective and suitable for controlling the second order NMP system because it can decide the magnitude of undershoot in a direct and stable way and reduce the response time compared with the conventional ones.

• International Journal of Aeronautical and Space Sciences
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• 제14권2호
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• pp.172-182
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• 2013

This paper focuses on the modeling and intelligent control of the new Eight-Rotor MAV, which is used to solve the problem of the low coefficient proportion between lift and gravity for the Quadrotor MAV. The Eight-Rotor MAV is a nonlinear plant, so that it is difficult to obtain stable control, due to uncertainties. The purpose of this paper is to propose a robust, stable attitude control strategy for the Eight-Rotor MAV, to accommodate system uncertainties, variations, and external disturbances. First, an interval type-II fuzzy neural network is employed to approximate the nonlinearity function and uncertainty functions in the dynamic model of the Eight-Rotor MAV. Then, the parameters of the interval type-II fuzzy neural network and gain of sliding mode control can be tuned on-line by adaptive laws based on the Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. The validity of the proposed control method has been verified in the Eight-Rotor MAV through real-time experiments. The experimental results show that the performance of the interval type-II fuzzy neural network based adaptive sliding mode controller could guarantee the Eight-Rotor MAV control system good performances under uncertainties, variations, and external disturbances. This controller is significantly improved, compared with the conventional adaptive sliding mode controller, and the type-I fuzzy neural network based sliding mode controller.