• 한국소음진동공학회논문집
• /
• 제25권11호
• /
• pp.753-763
• /
• 2015

This paper proposes a decentralized robust adaptive neural network control scheme using multiple radial basis function neural networks for electrically driven robot manipulators with bounded input voltages in the presence of uncertainties. The proposed controller considers both robot link dynamics and actuator dynamics. Practically, the controller gain coefficients applied at each joint may be nonlinear time-varying and the input voltage at each joint is saturated. The proposed robot controller overcomes the various uncertainties and the input voltage saturation problem. The proposed controller does not require any robot and actuator parameters. The adaptation laws of the proposed controller are derived by using the Lyapunov stability analysis and the stability of the closed-loop control system is guaranteed. The validity and robustness of the proposed control scheme are verified through simulation results.

• 제어로봇시스템학회논문지
• /
• 제20권2호
• /
• pp.163-169
• /
• 2014

Using the classical cascade voltage control strategy, this paper proposes an analytical solution to an MPC (Model Predictive Control) problem with a hexagonal input constraint set for the inner-loop to regulate the output voltage of the UPS (Uninterruptible Power Supply). Focus is placed on how to deal with the hexagonal input constraint set without any approximation. Following the conventional cascade voltage control strategy, the PI (Proportional-Integral) controller is used in the outer-loop in order to regulate the output voltage. The simulation results illustrate that the capacitor voltage rapidly goes to its reference in a satisfactory manner while keeping other state variables bounded under an unexpected load changes.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
• /
• pp.669-671
• /
• 2004

This paper presents a MIMO nonlinear controller for the power system consisting of a turbine and a synchronous generator connected to an infinite bus. The controller proposed is based on feedback input-output linearization; its main goal is to regulate the terminal voltage and frequency, and is to improve the transient stability under large disturbances and unexpected faults. It is guaranteed that the voltage converges to its reference value exponentially, and that the frequency and the mechanical/electrical power are bounded. The design procedure is tested on a single machine infinite bus power system through simulations, and is seen to be effective.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2003년도 봄 학술발표회 논문집
• /
• pp.403-410
• /
• 2003

A new semiactive control strategy for seismic response reduction using a neuro-controller and a magnetorheological (MR) fluid damper is proposed. The proposed control system adopts a clipped algorithm which induces the MR damper to generate approximately the desired force. The improved neuro - controller, which was developed by employing the training algorithm based on a cost function and the sensitivity evaluation algorithm replacing an emulator neural network, produces the desired active control force, and then by using the clipped algorithm the appropriate command voltage is selected in order to cause the MR damper to generate the desired control force. The simulation results show that the proposed semiactive neuro-control algorithm is quite effective to reduce seismic responses. In addition, the semi-active control system using MR fluid dampers has many attractive features, such as the bounded-input, bounded-output stability and small energy requirements. The results of this investigation, therefore, indicate that the proposed semi-active neuro-control strategy using MR fluid dampers could be effectively used for control of seismically excited structures.

• 한국지진공학회:학술대회논문집
• /
• 한국지진공학회 2003년도 춘계 학술발표회논문집
• /
• pp.473-480
• /
• 2003

A new semiactive control strategy for seismic response reduction using a neuro-controller and a magnetorheological (MR) fluid damper is proposed. The proposed control system adopts a clipped algorithm which induces the MR damper to generate approximately the desired force. The improved neuro-controller, which was developed by employing the training algorithm based on a cost function and the sensitivity evaluation algorithm replacing an emulator neural network, produces the desired active control force, and then by using the clipped algorithm the appropriate command voltage is selected in order to cause the MR damper to generate the desired control force. The simulation results show that the proposed semiactive neuro-control algorithm is quite effective to reduce seismic responses. In addition, the semiactive control system using MR fluid dampers has many attractive features, such as bounded-input, bounded-output stability and small energy requirements. The results of this investigation, therefore, indicate that the proposed semiactive neuro-control strategy using MR fluid dampers could be effective used for control seismically excited structures.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
• /
• pp.672-674
• /
• 2004

Many nonlinear controllers for the power system are based on nonlinear models involving the power angle as an element of the state, and therefore the reference value for the power angle is needed. As this reference value is not generally available, it is difficult to apply such nonlinear control methods in practice. To deal with this problem, we present an input-output feedback linearizing control scheme by selecting the output as a combination of the squared voltage and the relative frequency. It is shown that the internal dynamics are locally stable with controllable damping, and that the frequency remains bounded for all time. Simulations illustrate the effectiveness of the proposed method.

• 제어로봇시스템학회논문지
• /
• 제13권1호
• /
• pp.1-5
• /
• 2007

Many nonlinear controllers for the power system are based on nonlinear models involving the power angle as an element of the state, and therefore the reference value for the power angle is needed. As this reference value is not generally available, it is difficult to apply such nonlinear control methods in practice. To deal with this problem, we present an input-output feedback linearizing control scheme by selecting the output as a combination of the squared voltage and the relative frequency. It is shown that the internal dynamics are locally stable with controllable damping, and that the frequency remains bounded for all time. Simulations illustrate the effectiveness of the proposed method.

• 한국정보과학회논문지:시스템및이론
• /
• 제31권10호
• /
• pp.562-574
• /
• 2004

본 논문에서는 고정 우선순위 경성 실시간 시스템에 대한 에너지 측면에서의 최적의 전압 스케줄링 문제를 고려한다. 먼저, 이 문제가 NP-hard임을 증명한다. 다음으로 이 문제에 대한 fully polynomial time approximation scheme(FPTAS)을 제시한다 제안한 FPTAS는 주어진 임의의 $\varepsilon$>0에 대해 에너지 소모량이 최적의 전압 스케줄에 비해 (1＋$\varepsilon$)배 이내에 있는 전압 스케줄을 문제의 입력의 크기와 1/$\varepsilon$의 다항함수 이내의 시간에 계산해준다. 실험 결과, 제안된 FPTAS는 기존의 휴리스틱에 비해 더 효율적인 전압 스케줄을 더 빠른 시간에 찾아주었다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1991년도 하계학술대회 논문집
• /
• pp.587-590
• /
• 1991

It is general to make the circuit analysis of current-fed type inverter driven by current source with rippleless input under the assumption of infinite induction Ld in direct current reactor(DCL). This paper focusing on the fact that Ld has bounded value in real circuit, examines operating characteristics by analysis of static state characteristics of current type inverter driven by voltage source and compares it with the operating characteristics of the circuit driven by current source.

• Journal of Power Electronics
• /
• 제11권5호
• /
• pp.719-725
• /
• 2011

This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine lumped uncertainty. The overall system stability is proved by the Lyapunov theorem. It is shown that the torque and flux tracking errors as well as the updated weights of the ANN are uniformly ultimately bounded. Finally, effectiveness of the proposed control approach is shown by computer simulation results.