• Journal of information and communication convergence engineering
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• 제2권1호
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• pp.46-51
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• 2004

The problem of reducing the effect of an unknown disturbance on a dynamical system is one of the most fundamental issues in control design. We propose a robust PID (Proportional Integral Derivative) control method with neural network for improving the performance due to the rejection of an unknown disturbance. The proposed system consists of a model of the plant, a conventional PID controller and a multi-layer neural network, and is composed of two loop; the first loop enables the system to achieve stability of system, the second loop rejects an unknown disturbance. Simulation and experiment results show that the proposed method improves considerably on the performance of the conventional PID control method and the typical IMC method using neural network.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 추계종합학술대회
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• pp.945-948
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• 2003

본 논문에서는 시스템에 발생하는 외란의 영향을 최소로 하기 위해 다층 신경회로망을 이용한 강인 PID 제어 방식을 제안한다. 제안한 방식은 제어 시스템에 발생하는 불특정의 외란을 필터링 하기 위해 다층 신경망을 이용한다. 외란이 포함된 실제 플랜트의 입력을 신경망의 출력으로 보상하여 플랜트를 순수하게 PID 제어기로만 제어하도록 한다. DC 모터에 대한 시뮬레이션과 MM-LDM(Moving Magnetic Linear DC Motor)의 위치제어 실험을 통해 제안한 알고리즘의 유용성을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.113.1-113
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• 2001

The robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The neural network structure presents the bound function and does not need the concave property of the bound function, The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulators.

• Journal of Power Electronics
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• 제10권5호
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• pp.505-517
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• 2010

In this paper, an intelligent sliding-mode speed controller for achieving favorable decoupling control and high precision speed tracking performance of permanent-magnet synchronous motor (PMSM) drives is proposed. The intelligent controller consists of a sliding-mode controller (SMC) in the speed feed-back loop in addition to an on-line trained wavelet-neural-network controller (WNNC) connected in parallel with the SMC to construct a robust wavelet-neural-network controller (RWNNC). The RWNNC combines the merits of a SMC with the robust characteristics and a WNNC, which combines artificial neural networks for their online learning ability and wavelet decomposition for its identification ability. Theoretical analyses of both SMC and WNNC speed controllers are developed. The WNN is utilized to predict the uncertain system dynamics to relax the requirement of uncertainty bound in the design of a SMC. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode speed controller. An experimental system is established to verify the effectiveness of the proposed control system. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulated and experimental results confirm that the proposed RWNNC grants robust performance and precise response regardless of load disturbances and PMSM parameter uncertainties.

• 조명전기설비학회논문지
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• 제12권1호
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• pp.111-116
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• 1998

산업 자동화의 고정밀도에 따라 직류서보 전동기는 강인제어가 요구되고 있다. 하지만 PID 제어기를 갖는 전동기 제어 시스템이 부하 외란의 영향을 받게되면 제어 시스템의 강인제어는 어렵게 된다. 이에 대한 보완적인 한 방법으로 본 논문에서는 전동기 제어시스템을 위한 PID-신경망 복합형 제어기법을 제시하였다. 신경망 제어기의 출력은 부하 외란 인가시에 발생되는 오차와 오차 변환율에 의해서 결정된다. 신경망 제어기를 이용한 직류서보 전동기의 강인제어는 시abf레이션에 의하여 확인하였다.

• Journal of Mechanical Science and Technology
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• 제18권11호
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• pp.1916-1922
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• 2004

A robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, and etc. Therefore, uncertainties are often nonlinear and time-varying. The neural network structure presents the bound function and does not need the concave property of the bound function. The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulator.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제49권8호
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• pp.557-564
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system can be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. In addition, the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Signal Processor DS1102 Board (TMS320C31).

• 로봇학회논문지
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• 제19권2호
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• pp.139-148
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• 2024

In this paper, we propose a two-degree-of-freedom snake robot head system and an I-PID (Intelligent Proportional-Integral-Derivative)-based controller utilizing RBF (Radial Basis Function) neural network and adaptive robust terms as a control strategy to reduce rotation occurring in the snake robot head. This study proposes a two-degree-of-freedom snake robot head system to avoid complex snake robot dynamics. This system has a control system independent of the snake robot. Subsequently, it utilizes an I-PID controller to implement a control system that can effectively manage rotation at the snake robot head, the robot's nonlinearity, and disturbances. To compensate for the time delay estimation errors occurring in the I-PID control system, an RBF neural network is integrated. Additionally, an adaptive robust term is designed and integrated into the control system to enhance robustness and generate control inputs responsive to signal changes. The proposed controller satisfies stability according to Lyapunov's theory. The proposed control strategy was tested using a 9-degreeof-freedom snake robot. It demonstrates the capability to reduce rotation in Lateral undulation, Rectilinear, and Sidewinding locomotion.

• 조명전기설비학회논문지
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• 제18권1호
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• pp.85-89
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• 2004

산업자동화의 고정밀도에 따라 궤환 제어시스템은 강인한 제어가 요구되고 있다. 하지만 신경망 궤환 제어시스템이 외란의 영향을 받았을 때, 시스템의 강인한 제어는 어렵게 된다. 본 논문에서는 이러한 문제를 해결하기 위한 한 방법으로 신경회로망제어기와 PR제어기의 복합형 제어방법을 제시하였다. 신경회로망 제어기는 주 제어기로서 동작하고, PID제어기는 허용오차가 경계영역을 벗어날 때 동작하는 보조제어기로 사용된다. 신경회로망-PID복합형제어기의 강인성은 전동기의 속도제어에 의해서 확인하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.620-623
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a fedforward recall and error back-propagation training. Since the total number of nodes are only eight this system can be easily realized by the general microprocessor. During the normal operation the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. IN addition the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Singal Processor DS1102 Board (TMS320C31) The basic DSP software is used to write C program which is compiled by using ANSI-C style function prototypes.