• Journal of Power Electronics
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• 제19권3호
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• pp.771-783
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• 2019

Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.

• Journal of Power Electronics
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• 제16권2호
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• pp.598-609
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• 2016

The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, a backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.

• Journal of Power Electronics
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• 제11권4호
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• pp.393-400
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• 2011

The main objective of this paper is to control the speed of Nonlinear Hybrid Electric Vehicle (HEV) by controlling the throttle position. Various control techniques such as well known Proportional-Integral-Derivative (PID) controller in conjunction with state feedback controller (SFC) such as Pole Placement Technique (PPT), Observer Based Controller (OBC) and Linear Quadratic Regulator (LQR) Controller are designed. Some Intelligent control techniques e.g. fuzzy logic PD, Fuzzy logic PI along with Adaptive Controller such as Self Organizing Controller (SOC) is also designed. The design objective in this research paper is to provide smooth throttle movement, zero steady-state speed error, and to maintain a Selected Vehicle (SV) speed. A comparative study is carried out in order to identify the superiority of optimal control technique so as to get improved fuel economy, reduced pollution, improved driving safety and reduced manufacturing costs.

• 전력전자학회논문지
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• 제16권6호
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• pp.594-601
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• 2011

본 논문은 상용의 전원 전압이 불평형 및 왜곡을 갖는 경우, 전원전압 불평형 및 왜곡 보상기능을 갖는 전류제어기를 제안한다. 일반적으로 3상 전원 시스템은 공통 입력 단자 (Point of Common Coupling)에 단상 부하 및 비선형 부하가 3상 부하와 같이 연결될 수 있어, 종종 불평형과 왜곡이 발생한다. 이런 조건하에서 3상 PWM 정류기를 일반적인 전류 제어기로 제어 할 경우, 3상 PWM 정류기 입력 전류가 불평형 및 왜곡을 갖는 문제가 발생한다. 본 논문에서는 간단한 모델 기반 적응 제어 시스템(Model Reference Adaptive System)기법을 이용하여 3상 입력 전압의 불평형 및 왜곡을 관측하고 전향 보상하는 전류 제어기를 제안하며, 모의해석과 실험을 통하여 그 효용성을 입증한다.

• 전기학회논문지
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• 제67권11호
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• pp.1423-1433
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• 2018

Recently, a permanent magnet synchronous motor of middle and small-capacity has high torque, high precision control and acceleration / deceleration characteristics. But existing control has several problems that include unpredictable disturbances and parameter changes in the high accuracy and rigidity control industry or nonlinear dynamic characteristics not considered in the driving part. In addition, in the drive method for the control of low-vibration and high-precision, the process of connecting the permanent magnet synchronous motor and the load may cause the response characteristic of the system to become very unstable, to cause vibration, and to overload the system. In order to solve these problems, various studies such as adaptive control, optimal control, robust control and artificial neural network have been actively conducted. In this paper, an incremental encoder of the permanent magnet synchronous motor is used to detect the position of the rotor. And the position of the detected rotor is used for low vibration and high precision position control. As the controller, we propose augmented state feedback control with a speed observer and first order deadbeat disturbance observer. The augmented state feedback controller performs control that the position of the rotor reaches the reference position quickly and precisely. The addition of the speed observer to this augmented state feedback controller compensates for the drop in speed response characteristics by using the previously calculated speed value for the control. The first order deadbeat disturbance observer performs control to reduce the vibration of the motor by compensating for the vibrating component or disturbance that the mechanism has. Since the deadbeat disturbance observer has a characteristic of being vulnerable to noise, it is supplemented by moving average filter method to reduce the influence of the noise. Thus, the new controller with the first order deadbeat disturbance observer can perform more robustness and precise the position control for the influence of large inertial load and natural frequency. The simulation stability and efficiency has been obtained through C language and Matlab Simulink. In addition, the experiment of actual 2.5[kW] permanent magnet synchronous motor was verified.

• 전기학회논문지
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• 제65권12호
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• pp.2053-2056
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• 2016

In this paper, we propose an output feedback tracking control method for the wheeled mobile robots with kinematic disturbances. The kinematic disturbances should be compensated to avoid the performance degradation. Also, the unavailable velocity of the mobile robot should be estimated. These should be estimated together by designing the nonlinear observer. Based on these estimates, the output feedback controller can be designed. The stability of the mobile robot control systems using the proposed method is rigorously analyzed and the simulation results are also provided to validate the proposed method.

• 제어로봇시스템학회논문지
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• 제12권11호
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• pp.1081-1087
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• 2006

Generally, biped walking is difficult to control because a biped robot is a nonlinear system with various uncertainties. In this paper, we propose a hybrid sliding-mode control method using a WNN uncertainty observer for stable walking of the 5-link biped robot with model uncertainties and the external disturbance. In our control system, the sliding mode control is used as main controller for the stable walking and a wavelet neural network(WNN) is used as an uncertainty observe. to estimate uncertainties of a biped robot model, and the error compensator is designed to compensate the reconstruction error of the WNN. The weights of WNN are trained by adaptation laws that are induced from the Lyapunov stability theorem. Finally, the effectiveness of the proposed control system is verified through computer simulations.

• 조명전기설비학회논문지
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• 제19권2호
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• pp.114-123
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• 2005

서보 설비를 위한 순차적 파라미터 자동 튜닝 알고리즘을 갖는 영구자석 동기전동기(Permanent Magnet Synchronous Motor: PMSM)의 비선형 속도 제어 기법이 제시된다. 비선형 제어 방식은 시스템 파라미터와 부하상태가 정확하게 일치하지 않는 경우 만족스러운 성능을 줄 수 일다. 최근에 향상된 속도 제어 성능을 위해 적응형 기법, 슬라이딩 모드 제어, 및 관측기에 기반 한 기법들이 제안되었다. 하지만, 이 방법들은 관성의 큰 변화, 속도의 빠른 과도 특성 및 샘플링 주기 증가와 같은 특정한 조건에서 더 이상 만족스러운 성능을 제공하지 않는다. 일반적으로, 속도 동특성에 영향을 주는 주요 파라미터를 동시에 추정하기는 쉽지 않다. 이를 해결하기 위해 설치 및 기동시 전동기 주요 파라미터를 시간 구간 별로 나누어 자동으로 추정하는 순차적 파라미터 튜닝 알고리즘이 제안된다. 제안된 방식이 DSP TMS320C31을 이용하여 구현되고 유용성이 시뮬레이션과 실험을 통해 입증된다.

• 제어로봇시스템학회논문지
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• 제22권5호
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• pp.320-325
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• 2016

This paper presents a robust finite-time sliding mode control (SMC) scheme for unknown disturbance and unmodeled nonlinear friction and dynamics in the robotic manipulator. A finite-time SMC (FSMC) surface and finite-time sliding mode controller are constructed to obtain faster error convergence than the conventional infinite-time based SMC. By adding prescribed constraint control term to a finite-time SMC to compensate for unknown disturbance and uncertainties, a robust control scheme can be designed as well as faster convergence control. In addition, simpler controller structure is built by using feed-forwarding upper bound coefficients of each manipulator dynamic parameters instead of model-based control or adaptive observer to estimate unknown manipulator parameters. Simulation and experimental evaluations highlight the efficacy of the proposed control scheme for an articulated robotic manipulator.