• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.239-240
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• 2007

In this paper, a robust adaptive backstepping controller will be proposed for the speed control of permanent magnet synchronous motors in using electrical vehicles. Stator resistance, damping coefficient, load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the Proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.751-753
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• 1996

The $H_{2}$/$H_{\infty}$ robust controller is designed by using polynomial approach. This controller can minimise a $H_{2}$ norm of error under the fixed bound of $H_{\infty}$ norm of mixed sensitivity function by employing the Youla parameterization and using polynomial approach at the same time. It is easy to apply this controller to adaptive system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.156-160
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• 1993

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an inproved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing force coming from the difference between th actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.323-326
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• 1996

In a simple system, the control schemes work well provided that the characteristic of the plant or the coefficients are known and fixed. But the condition is not met in the system like satellite, for example, varying over time and the coefficients of dynamic system change due to disturbance, etc, and the better precise model is required to control the given dynamic system well. Conversely, the fixed controller make the unmodel dynamic system with a wide class of modelling error be stable within the error tolerance limits. Also, a robust model reference adaptive control scheme is designed for the plant, paying attention to the derivation of the appropriate parametric model and the design of the normalizing signal to guarantee that it has the desired properties.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.330-335
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• 1998

In this paper the robust vector control method of induction motor for the purpose of improving the system performance deterioration caused by parameter variations is proposed. The estimations of the stator current and the rotor flux are obtained by the full order state observer with corrective prediction error feedback. and the adaptive scheme is constructed to estimate the rotor speed with the error signal between real and estimation value of the stator current. Adaptive sliding observer based on the variable structure control is applied to parameter identification. Consequently predictive current control and speed sensorless vector control can be obtained simultaneously regardless of the parameter variations.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.990-993
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• 2005

This paper presents new results on designing a robust adaptive direct controller for a class of non-linear first order systems. The designing method based on the use of dead zone in the parameters' update law. It is shown that the size of the dead zone does not depend on the upper bounds of the disturbances. That means that even if the bounds are large, the tracking error will always converge to a set of the dead zone size. However, in the ideal case, when the exogenous signal functions and the function represents un-modeled dynamics of the systems equal to zero, the proposed controller does nt mean the convergence to zero of the tracking error. Computer simulation results show the effectiveness of the controller in dealing with the stated problems.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.77-80
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• 1990

This paper presents a continuous-time robust direct adaptive algorithm in the presence of bounded disturbances and / or unmodeled dynamics. In the new algorithm, Narendra's adaptation law is adapted. And a term, proportional to the product of tracking error and normalizing signal, is added to the conventional control law. It is shown that the performance of the adaptive schemes is improved if a proportional adaptation tera is added to the control law. The scalar case is only discussed in the stability analysis. Computer simulation is presented to complement the theoretical result.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.158-161
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• 1996

A robust adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs an adaptive fuzzy system to compensate for the uncertainty of the plant. In order to improve the robustness under approximation errors and disturbances, the proposed architecture includes deadzone in adaptation laws. Unlike the previously proposed schemes, the magnitude of approximate errors and disturbances is not required in the determination of the deadzone size, since it is estimated using the adaptation law. The proposed algorithm is proven to be globally stable in the Lyapunov sense, with tracking errors converging to the proposed architecture.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.38-47
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• 2001

In this paper, a new scheme of adaptive-neuro control system is presented to implement real-time control of robot manipulator using Digital Signal Processors. Digital signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of therir prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust perfor-mance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for the implementation of real-time control of robot system by the simulation and experi-ment.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.533-536
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• 2000

We consider the design of quadrature amplitude modulation (QAM) transceivers f3r fixed wireless communications. The use of adaptive power control in the transmitter (Tx) can provide BER performance robust to fading and improved BER performance. The BER performance is evaluated by analytical and simulation results when multi-level QAM transceiver employing power control in the Tx is applied to fixed wireless channel with flat fading and frequency selective fading. The effect of power control parameters such as power control range and power control step size is investigated