• ICROS
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• v.3 no.5
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• pp.58-64
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• 1997

pH 공정은 비선형이며, 입력흐름의 성분과 농도가 바뀜에 따라 비선형성이 급격히 바뀌는 적응제어가 요구되는 공정이다. 최근 이 공정에 대한 모델링 기법이 확립되고, 매우 작은 수의 변수를 갖는 parameterization이 제안되어 간단한 형태의 우수한 적응제어법이 구성되어 있다. 그러나 estimation의 windup은 선형 시스템의 적응제어법에서와 마찬가지로 큰 문제점으로 남아있다. 본 고에서는 pH 공정의 적응제어법을 간략히 살펴보았으며 좀 더 견실하고 우수한 성능을 주는 방법을 위하여 두 가지 제안을 하였다. 한 제안은 기존의 변수 estimation의 목적함수가 제어기 성능에 직접적이지 못한 것을 바로 잡으려는 것이다. 새로 제안한 것을 적응제어에 바로 이용하는데 아직 걸림돌이 몇몇 남아 있어 연구가 요구되고 있다. 또 한 제안은 estimation windup을 해결하려는 것으로 pH 공정 특성상 나타나는 것으로 바로 pH 공정 적응제어에 이용될 수 있다.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.510-511
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• 2010

본 논문은 계통연계형 인버터의 정현파 전류제어를 위한 직접 적응제어 기법을 제안한다. 기존의 계통연계형 인버터 전류제어기에 비해 고조파 성분 제거 능력이 우수하고 소프트웨어적으로 쉽게 구현할 수 있는 장점이 있다. LC필터의 L값의 변동에 의한 제어기 내부의 파라미터가 발산하는 문제를 Resetting기법을 적용하여 보완하였다. 본 논문에서 제안한 정현파 전류 적응제어기의 우수성을 시뮬레이션과 실험을 통해 검증하였다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.8
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• pp.140-146
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• 1995

In case the uncertainty existing in a system is assumed to satisfy the matching condition and to be come-bounded. Y. H. Chen proposed an adaptive robust control algorithm which introduced adaptive sheme for a design parameter into robust deterministic controls. In this paper, the adaptive robust control algorithm is applied to the position tracking control of direct drive robots, and simulation and experimental studies are conducted to evaluate control performance.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.6
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• pp.1346-1360
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• 1989

본 연구에서는 기준 모델 적응제어 방식에서 직접 적응제어 방식을 사용하여 부하의 변동 및 외란이 발생할 경우에도 매니퓰레이터의 정확한 궤적의 추종 및 속도 의 실시간 제어가 가능한 적응제어시스템을 설계하고자 한다. 제2절에서는 로봇 매니퓰레이터의 기구학적 이론 및 동적 모델링에 대한 기본이론을 전개하고, 제3절 에서는 제어시스템의 설계를 위한 제어 알고리즘과 초안정(hyperstability)이론을 통한 안정성 해석을 다룬다. 그리고 제4절에서는 제안된 제어기의 성능 평가를 위해 6관절 로봇인 스탠포드 로봇 매니퓰레이터에 대한 시뮬레이션을 통한 결과를 토오크 계산법(computed torque method)에 의한 결과와 비교 검토함으로서 제안된 제어기의 성능을 예증한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.564-570
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• 2003

This paper presents an adaptive fuzzy control scheme for nonlinear helicopter system which has uncertainty or unknown variations in parameters. The proposed adaptive fuzzy controller is a model reference adaptive controller. The parameters of fuzzy controller are adjusted so that the plant output tracks the reference model output. It is shown that the adaptive law guarantees the stability of the closed-loop system by using Lyapunov function. Several experiments with a small model helicopter having parameter variations are performed to show the usefulness of the proposed adaptive fuzzy controller.

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.559-559
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• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.59-69
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• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.2926-2934
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• 2000

This study presents a direct adaptive controller which is derived by using Lyapunovs direct methods for trajectory tracking control of a pneumatic cylinder. The structure of the controller is very simple and computationally efficient because it does not use either the dynamic model or the parameter values of the pneumatic system. The bounded stability of the system is shown in the presence of the bounded unmodeled dynamics. The bounded size of tracking errors can be made arbitrarily small without giving andy influences on either input or output variables. The trajectory tracking performance and the stability of the control system is verified experimentally. The results of the experiments show that the proposed controller tracks the given trajectories, sine function and cycloidal function trajectories, more accurately than PD controller does, and it stabilizes the system and adaptive variables.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.