• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.72-72
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• 2000

This paper presents a methodology combining sliding mode control and fuzzy control to tune the boundary layer and input gain according to the system state. The equivalent control is designed such that the nominal system exhibits desirable dynamics, The robust control with fuzzy self-tuning is then developed to guarantee the reaching condition and reduce chattering phenomenon in the presence of parameter and disturbance uncertainties.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.519-520
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• 2016

BLSRM is a nonlinear, strong coupling and multi-variable system. The conventional control method is vulnerable to uncertain factors such as the load disturbance and satellite parameters change. It is difficult to obtain satisfactory control effect. Basing on a 8/10 BLSRM, whose suspending force control is separated with the torque control, this paper presents adaptive fuzzy PID controller for levitation control, which apply the fuzzy logic control to the conventional PID controller for parameters self-tuning. Both fuzzy and parameters of PID controller are self-tuning on-line, which improve the performance of controller. Finally, simulation and experimental results show the performance of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.9
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• pp.2153-2161
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• 1993

Self-tuning control algorithms for an input-amplitude constrained system are developed and implemented. Magnitude of control input for small motors is generally restricted to narrow bound due to actuator saturation. The gain-adjusted control algorithm and the bounded-gain control algorithm proposed in this study yield smoother control input variations within the magnitude constraints comparing with the existing Clarke's suboptimal control algorithm. In the gain-adjusted control algorithm, the feedforward gain is adjusted using maximum gain, while in the bounded-gain control algorithm, the feedforward gain is bounded using weighting factor. For the DC servo motor control, the system performances of the proposed algorithms are compared with those of the existing algorithm by computer simulation and experiment. It is shown that the input variations of the proposed algorithms are smoother as compared with the existing algorithm.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.223-226
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• 1991

In this paper, the problem of designing on adaptive controller for dc drives using state observers, which is operated under varying load conditions, is addressed. A robust self-tuning controller that can track a constant reference and reject constant load disturbances is also studied. This scheme is very attractive since the estimates of system parameters are available in real time. Parameter estimation is based on the recursive least squares method and the control algorithm of the pole placement technique. Also, state observer systems are applied. State observer systems are required to estimate the states quickly and exactly without being affected by the disturbances.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.66-68
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• 2004

PID제어기를 플랜트 파라미터를 이용하여 구성하는 IMC-PID제어에 대해 연구한다. 특히 변하는 플랜트에 대해서는 자기동조(ST, Self-Tuning)를 사용하여 시스템을 식별하여 활용한다. 특히 실시간으로 개루프의 위상여유 및 이득여유를 모니터링하여 정해진 구역을 벗어나게 되면 식별된 시스템파라미터를 이용하여 IMC-PID제어기를 구성한다. 또 시간영역 지표로 과도한 오차가 발생하는 경우에도 제어기를 갱신함으로서 전체적으로 보면 견실 PID제어기 형태를 갖게 한다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.122-132
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• 1993

In this paper, a self tuning control algorithm is proposed for the high performance drive of DC servo motor, which is adequate to the servo system having frequent load variation. In order to realization of the algorithm, the control system is developed using a fixed point high speed digital signal processor. TMS320C25. Control algorithm is composed of two parts. One is estimation law part using recursive least mean square method, the other is control law part using minimum variance control method. For the purpose of easiness of applying adaptive algorithm, developed control system is based o PC-DSP structure which can develop, debug programs and monitor the dynamic behaviors,etc. Through computer simulation and experimental results, it was verified that proposed control system could estimate system parameters and was robust to the variation of the load and as a result, was adequate to the servo motor drives.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1071-1084
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• 1992

In this paper, the method for navigation and obstacle avoidance of the autonomous mobile robot is proposed. The proposed algorithms are based on the fuzzy inference system which is able to deal with imprecise and uncertain information. The self-tuning algorithm, which adopts the simplex method, modifies the parameters of membership functions of the input-output linguistic variables by changing the support of these fuzzy sets according to the integral of absolute error(IAE) of the system response. The wall-follwing navigation and obstacle avoidance of the mobile robot are based on range data measured from the internal sensors(encoder) and the outer sensors(sonar sensor). In addition, the algorithm for the obstacle detection proposed in this paper is based on the expert's experience. Finally, the effectiveness of navigation and obstacle avoidance algorithm is demonstrated through simulation and experiment.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.95-98
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• 2001

The adaptive line enhancer (ALE) is widely used for enhancing narrowband signals corrupted by broadband noise. In this paper, we propose novel ALE methods to improve the enhancing capability. The proposed methods are motivated by the fact that the output of the ALE is a fine estimate of the desired narrowband signal with the broadband noise component suppressed. The proposed methods preprocess the input signal using ALE filter to regenerate a finer input signal. Thus the proposed ALE is driven by the input signal with higher signal-to-noise ratio (SNR). The analysis and simulation results are presented to demonstrate that the proposed ALE has better performance than conventional ALE´s.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.375-378
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• 1991

This paper presents a design method of synchronous generator excitation controller using self-tuning PID algorithm. Controller parameter is determined by using adaptive control theory in order to maintain optimal operation of generator under the various operating conditions. To determine the optimal parameter of controller. minimum variance algorithm using the recursive leastsquare(RLS) indentification method is adopted and the difference between the speed deviation with weighted factor and voltage deviation is used as the input signal of adaptive controller, which provides good damping and conversion characteristics. The results tested on a single machine infinite bus system verify that the proposed controller has better dynamic performances than conventional controller.