• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.753-763
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• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.163-169
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• 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.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.669-671
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• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.403-410
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• 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.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.473-480
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• 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.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.672-674
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• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.1-5
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• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.10
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• pp.562-574
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• 2004

We address the problem of energy-optimal voltage scheduling for fixed-priority hard real-time systems. First, we prove that the problem is NP-hard. Then, we present a fully polynomial time approximation scheme (FPTAS) for the problem. for any $\varepsilon$>0, the proposed approximation scheme computes a voltage schedule whose energy consumption is at most (1+$\varepsilon$) times that of the optimal voltage schedule. Furthermore, the running time of the proposed approximation scheme is bounded by a polynomial function of the number of input jobs and 1/$\varepsilon$. Experimental results show that the approximation scheme finds more efficient voltage schedules faster than the best existing heuristic.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.587-590
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• 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
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• v.11 no.5
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• pp.719-725
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• 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.