• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.95-103
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• 1999

수중운동체가 수중에서 진행할 때 외부 파도에 의한 불확실한 부하 저항을 받으므로 이에 대응하는 핀(조타) 구동부의 제어 문제를 고려한다. 본 논문에서 제시하는 제어기는 본체로부터의 지정 각도를 부여 받으면 이에 부응하여 핀의 각도와 각속도를 이용하여 제어기의 알고리즘을 구축하여 지정된 경로를 추적하게 한다. 또한 핀의 각속도 정보의 이용이 부득이 어려운 상황에 대처하기 위하여 핀의 각도만을 이용한 출력제어기나 추정기를 설계하여 주위 환경의 불확실성을 극복할 수 있는 제어기를 제안한다. DC서보 모터로 구성괸 핀 구동부에 대해 실제 데이터를 사용하여 제안된 제어기의 성능을 시뮬레이션을 통하여 검증한다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1322-1331
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• 1989

This paper proposes a new adaptation algorithm with which a model reference adaptive control can give a local boundedness of the tracking error applied to a continuous-time linear time-invariant single-input single-output plant whose reduced-order model is of relative degree 1 and whose unmodeled dynamics may be represented in a sigular perturbation form. With the addition of an offset term and an extra adaptation structure, this algorithm is shown to have a robustness property in the sense that this gives zero residual tracking errors when the unmodeled dynamics are disappeared.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.7
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• pp.1158-1168
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• 1994

A variable structure control scheme(VSCS) with sliding mode that can be applied to the process with input/output(I/O) delay is proposed and its control performances is evaluated. The proposed VSCS with and output feedback scheme comprises a variable structure controller, a servo dynamic for tracking the set-poing, and a Smith predictor for compensating the effects of time delay. The robustness against the parameter variations and external disturbances can be achieved by the proposed VSCS even when the controlled process includes I/O delay. And the desired transient response is obtained by simple adjustment of the coefficients of the switching surface equation.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1868-1870
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• 2006

Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.353-356
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• 1997

Hydraulically driven manipulators are superior to electrically driven ones in the power density and electrical insulation. But an electro-hydraulic manipulator using hydraulic actuators has many nonlinear elements, and this parameter fluctuations are greater than those of electrically driven manipulator. So this is relatively difficult to realize not only stable contact work but also accurate force control for the autonomous field task such as the maintenance task of high voltage active electric line or the automatic excavation task by hydraulic excavator. In this report, we propose robust force control algorithm, which can be applied to there real field task such as the construction field, nuclear plant and so on. Proposed force controller has the same structure as that of disturbance observe for position control. The difference between force and position disturbance observer is that the input and output of disturbance observer are forces in the case force disturbance observer and the plant varies much compared to the case of position control. In the design of force disturbance observer, generalized plant is derived and the stabilized filter is designed by H infinity control theory to ensure the robuts t stability even though the stiffness of environment changes from sponge to steel, and the contact surface also changes from flat to round shape. Experimental results show that highly robust force tracking by a 6-link electro-hydraulic manipulator could be achieved under various environment conditions.

• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.88-97
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• 2013

This paper presents a robust air-to-fuel ratio (AFR) control algorithm for managing exhaust gas recirculation (EGR) systems. In order to handle production tolerance, deterioration and parameter-varying characteristics of the EGR system, quantitative feedback theory (QFT) is applied for designing the robust AFR control algorithm. A plant model of EGR system is approximated by the first order transfer function plus time-delay (FOPTD) model. EGR valve position and AFR of exhaust gas are used as input/output variables of the plant model. Through engine experiments, parameter uncertainty of the plant model is identified in a fixed engine operating point. Requirement specifications of robust stability and reference tracking performance are defined and these are fulfilled by the following steps: during loop shaping process, a PID controller is designed by using a nominal loop transmission function represented on Nichols chart. Then, the frequency response of closed-loop transfer function is used for designing a prefilter. It is validated that the proposed QFT-based AFR control algorithm successfully satisfy the requirements through experiments of various engine operating points.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.2
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• pp.107-114
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• 2014

This paper proposes a common rail pressure control algorithm for passenger car diesel engines. For handling the parameter-varying characteristics of common rail systems, the quantitative feedback theory (QFT) is applied to the design of a robust rail pressure control algorithm. The driving current of the pressure control valve and the common rail pressure are used as the input/output variables for the common rail system model. The model parameter uncertainty ranges are identified through experiments. Rail pressure controller requirements in terms of tracking performance, robust stability, and disturbance rejection are defined on a Nichols chart, and these requirements are fulfilled by designing a compensator and a prefilter in the QFT framework. The proposed common rail pressure control algorithm is validated through engine experiments. The experimental results show that the proposed rail pressure controller has a good degree of consistency under various operating conditions, and it successfully satisfies the requirements for reference tracking and disturbance rejection.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.218-232
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• 2022

Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.

• Journal of Power Electronics
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• v.19 no.4
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• pp.835-845
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• 2019

This paper presents a robust-optimal control strategy to improve the output-voltage error-tracking and control capability of a DC-DC boost converter. The proposed strategy employs an optimized Fractional-order Proportional-Integral (FoPI) controller that serves to eliminate oscillations, overshoots, undershoots and steady-state fluctuations. In order to significantly improve the error convergence-rate during a transient response, the FoPI controller is augmented with a pre-stage nonlinear error-modulator. The modulator combines the variations in the error and error-derivative via the signed-distance method. Then it feeds the aggregated-signal to a smooth sigmoidal control surface constituting an optimized hyperbolic secant function. The error-derivative is evaluated by measuring the output-capacitor current in order to compensate the hysteresis effect rendered by the parasitic impedances. The resulting modulated-signal is fed to the FoPI controller. The fixed controller parameters are meta-heuristically selected via a Particle-Swarm-Optimization (PSO) algorithm. The proposed control scheme exhibits rapid transits with improved damping in its response which aids in efficiently rejecting external disturbances such as load-transients and input-fluctuations. The superior robustness and time-optimality of the proposed control strategy is validated via experimental results.