• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.196-200
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used fur on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.273-275
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• 1992

A dynamic system containing uncertain elements is considered. The upper bound of the values of these uncertainties is estimated. Using the estimated value a bounded error state feedback control is proposed based on Corless and Leitmann's result [1].

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.136-139
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.24-29
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• 1990

In this paper ,ie propose a hybrid position/force controller of a robot manipulator using double-layer neural network. Each layer is constructed from inverse dynamics and Jacobian transpose matrix, respectively. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using a PUMA 560 manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.734-738
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• 1996

An adaptive output feedback controller is designed for tracking control of an n-link robot manipulator with unknown load. High-gain observers with same structure as error dynamic systems are used to estimate joint velocities. The parameter adaptation is achieved by the smoothed projection algorithm. The control inputs are saturated outside a domain of interest. Simulation results on a 2-link manipulator illustrate that when the speed of the high-gain observer is sufficiently high, the proposed controller recovers the performance under state feedback control.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.996-1007
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• 1996

In this paper, a blind adaptation method for a decision feedback equalizer (DFE) is proposed to deal with nominimum phase channels. This equalizer is composed of a linear transversal filter and a prediction error filter which are trained separately using constant modulus and decision feedback prediction algorithms, respectively, during the learnign time. The proposed algorithm guaranetees the DFE to converge to a suboptimal point on the condition that a linear transversal of the proposed scheme is illustrated and the performance is compared with conventional blind equlization algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.195-198
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• 1997

This paper proposes the effective compensation method of the rotor time constant of induction motor. An indirect vector control method is highly dependent on the motor parameters. To solve the problem of performance degradation due to parameter variation in an indirect vector control of induction motor, we compensate the rotor time constant by current error feedback. The proposed method is a simple on-line rotor time constant compensation method using the information from terminal voltages and currents. As the current error, difference between current command and estimated current, approaches to zero, the value of rotor time constant in an indirect vector controller follows the real value of induction motor. This scheme is valid transient region as well as steady state region regardless of low or high speed. This method is verified by computer simulation. For this, we constructed the simulation model of induction motor, indirect vector controller and current regulated PWM (CRPWM) voltage source inverter (VSI) using SIMULINK in MATLAB.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.2
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• pp.61-71
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• 2005

The study reported deals with investigating the feasibility of control strategy for a serial rigid link manipulator that applies impulsive torques at the joints. The strategy is illustrated for a planar three rigid link manipulator. An impulse-based concept which uses successive torque impulses on rigid link as the controller for motion correction was introduced. This control strategy was tested over the entire trajectory to demonstrate that the tracking error could be reduced effectively. The best condition for minimizing the tracking error with the least impulse input at each joint is investigated by considering one design and one operating parameter. The first was the damping in the system, and the second was the sampling time during operation. The results show that this approach can provide useful guidance for the design and control of robot manipulators that require minimum impulse feedback for accurate tracking.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.746-748
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• 1988

A way of improving the transient performance is suggested for a class of model reference adaptive control systems. To increase the convergence rate of a model following error, an error feedback term is incorporated into the control law.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.10
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• pp.938-946
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• 2012

Interference alignment (IA) is a data transmission technique that achieves the maximum degrees-of-freedom (DoF) in the multiuser interference channel for high signal-to-noise ratios (SNRs). However, most prior works on IA are based on the unrealistic assumption that perfect and global channel-state information (CSI) is available at all transmitters and receivers. In this paper, we propose the efficient design of feedback framework for IA that substantially suppresses the feedback overhead. While the feedback overhead in the conventional IA quadratically increases with K, the proposed feedback scheme supports the sequential exchange of computed IA precoders between transmitters and receivers and reduces the feedback overhead that linearly scales with K. Moreover, we analyze the residual interference due to the quantization error in limited feedback and propose the ordered IA algorithm that selects IA pair to minimize the sum residual interference in given channel realizations.