• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.49-52
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• 1988

This paper deal with the robust high gain controller design technique by inner-loop feedback, output feedback, feedforward compensation, and presented the algorithm to be required the determination of gain matrix. Using this control scheme, simulation results have bean tested for the linearized model of the aircraft.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1307-1309
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• 1996

This paper presents hybrid active noise control (HANC) systems which is based on the combination of feedforward and feedback ANC controllers. HANC systems use FIR filters and is based on primary noise regeneration principle and filtered-X LMS algorithms. HANC systems show better attenuation characteristics and residual spectrum. The order of adaptive filters used in HANC systems is lower than that of conventional feedforward and feedback ANC systems. A proposed HANC algorithm was implemented using a Taxas Instruments TMS320C31 digital signal processor for experimental verification.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.2
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• pp.57-62
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• 2018

A brush-less type synchronous generator driven by an internal-combustion engine is used for emergency electric source. These types of generators have to maintain a certain range of output voltage even under the sudden load change conditions such as full load application and removal. This paper describes a method for suppressing the output voltage of a synchronous generator that operates excessively when the load fluctuates. The method used in this paper is a feedforward control method in which the main voltage control consists of a feedback loop using a typical PID controller and the load current is detected as a disturbance element and compensated directly. A feedforward system is constructed in which the load current is regarded as disturbance, and the appropriate feedforward controller configuration and parameters are found through simulation. Finally, it can be seen through the experiment that the feedforward control is performed properly. It can be seen that the generator terminal voltage is recovered to the steady state in a short period of time as compared with the existing PID control method even when the entire load of the generator is changed.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.10
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• pp.837-843
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• 2016

This paper presents a position control strategy for a pump-controlled electro-hydrostatic actuator (EHA) using feedforward control with disturbance compensation. As the disturbance observer is used to estimate nonlinear dynamics of EHA, which has valve-opening conditionals, as well as external disturbances, an additional feedforward control is adopted to achieve rapid response. The effectiveness of the proposed control strategy is verified through experiment using an EHA test bench. The proposed controller shows better tracking performance compared with a conventional PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.150-155
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• 1993

The neural network MRAC system is presented. The purpose of this paper is applied to a plant that is to be controlled in a strongly nonlinear environment. The proposed system has a learning and adaptive ability in the varying environment by using the back-propagation learning algorithm based on Lyapunov stability theory. N.N. regulator is a part of overall system and is guaranteed to be stable in initial stage. Nonlinear terms of the varying mass, colilori, centifugal, and gravity are compensated for by feedforward N.N. regulator. And the feedback controller (adaptive mechanism) works to eliminate errors of position, velocity which the feedforward controller cannot compensate for. Finally, the proposed system will be demonstrated by simulation of a two d.o.f robot manipulator.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.705-709
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• 1995

The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it was used as a commanded feedforward input generator. A PD feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the excavator as well as the PD feedback error. By using the BP network as a feedforward controller, no a priori knowledge on system dynamics is need. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbancen and performance improvement with the on-line learning in the position control of excavator attachment.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.147-150
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• 1996

This paper describes controller design and implementation method for controlling the temperature distribution in a thermal stratified wind tunnel(TSWT) by using a neural network algorithm. It is impossible to derive a mathematical model of the relation between heat inputs and temperature outputs in the test section of the TSWT governed by a nonlinear turbulent flow. Thus inverse neural network models with a multi layer perceptron structure are used in a feedforward control loop and feedback control loop to generate an arbitrary temperature distribution in the test section of the TSWT.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.693-696
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• 1988

This paper is investigated on the improvement for steam temperature control at Boryung coal-fired drum boiler type thermal power plant. The steam temperatur control has been mainly operated by the feedback controllers. Automatic controllers are bounded and difficult. Because boiler system is nonlinear and the system time delay is very large. Optimal regulators including predictive feedforward and differentiate control are synthesized and some improved output results are presented.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.105-108
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• 1989

This paper proposes a performance improvement to a control system with state feedback control loops and feedforward control loops proposed in a previous paper. The technique allows to solve the regulator problem and at the same time to make the system follow command signal. The scheme is implemented in a 16 bit microcomputer using a floating coprocessor. Tests on a DC machine have been conducted, comparisons with the previous results are made.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.236-241
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• 1988

This note considers the class of controllers with integral action which arise directly from appropriate system models. Via internal model principle approach, a corresponding class of self-tuning controller is shown to have both integral action in controller and offset removal in the tuning algorithm. The key idea is to constrain the estimator in each step in order to ensure that dc gain of feedforward and feedback polynomial of adaptive controller are always equal, thus allowing the loop integrator to work properly.