• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.305-308
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• 1988

Pole assignment controller with variable forgetting factor is generalizaed to allow the output and/or input variance to be reduced. The algorithm can give significant reductions in variance for little extra computational effort and is presented for servo-tracking using leat-squares estimation. Moreover, the use of a variable forgetting factor with correct choice of information bound can avoid 'blowing-up' of the covariance matrix of the estimates and subsequent unstable control.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.902-904
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• 1988

For a generalized minimum variance controller algorithm the weighting polynomials are are calculated in a way to assign the closed loop poles of the system and to specify the controller gain at a frequency. As a result the oscillations in the control signal may be reduced without changing the deterministic behaviour of the system.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.897-900
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• 1998

Through fuzzy logic controller is very useful to many areas, it is difficult to build up the rule-base by experience and trial-error. So, effective self-tuning fuzzy controller for the position control of ball and beam is designed. In this paper, we developed the neural network control system with fuzzy-neuron which conducts the adjustment process for the parameters to satisfy have nonlinear property of the ball and beam system. The proposed algorithm is based on a fuzzy logic control system using a neural network learinign algorithm which is a back-propagation algorithm. This system learn membership functions with input variables. The purpose of the design is to control the position of the ball along the track by manipulating the angualr position of the serve. As a result, it is concluded that the neural network control system with fuzzy-neuron is more effective than the conventional fuzzy system.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.273-277
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• 1988

This paper is to show design of robot manipulator which has 3-link using DC Motor and realization of control algorithm with IBM - XT Micro-computer connected. Gentral algorithm is applicated by position and pass control using point-to-point method. At first, this paper computes required angles on each joint in order to search desired position or path, and uses a voltage control with feedback from output of encoder and tachometer in real time. The application of control algorithm on position, velocity and force for each joint of manipulator by using self-tuning control is left for next study.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.726-728
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• 1995

A servo control algorithm robust under disturbance and reference change is developed using the self tuning control method based on the concept of the least distribution control. Also, the design algorithm incorporates the concepts of the well known internal model principle and the annihilator polynomial. In order to evaluate the effectiveness of the method, MAGLEV (Magnetic Levitation) system is used and the position control experiment for reference changes and disturbances of step type is done.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.738-741
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• 1995

This disseration realized the simple digital controller board using ${\mu}$-PD 70320 microprocessor has characteristics that are low cost, simple hardware organization, convenient and interchangeable with the 8086 for the servo system. We gave the control algorithm such as PD control. Self tuning adaptive control and Fuzzy control to the realized controller board and made a new real number data type for a high accuracy control. Users can select of suitable for the control algorithim. In the result of simulation and experiment shown a good performance.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.95-104
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• 1999

In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.1
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• pp.43-51
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• 1987

In this paper, a state-space model of the burnthrough point control system of an industrial sintering process is derived. The model is then used in designing a self-tuning controller which consists of the receding horizon control law and a least-squares prediction algorithm with covariance resetting. By applying this controller to POSCO IV sintering process, satisfactory experimental results have been obtained. This paper presents some of these real-time experimental results and analyzes the control performance through productivity, operation indices, quality, sintered material composition, etc. From these experimental results and simulation results, the validity of the model can be observed. Moreover, the properties of the controller, e.g. stability, steady-state error, are shown based on the model.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2278-2282
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• 2003

In this paper the structure of 2-DOF controller based on artificial immune network algorithms has been suggested for nonlinear system. Up to present time, a number of structures of the 2-DOF controllers are considered as 2-DOF (2-Degrees Of Freedom) control functions. However, a general view is provided that they are the special cases of either the state feedback or the modification of PID controllers. On the other hand, the immune network system possesses a self organizing and distributed memory, also it has an adaptive function by feed back law to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation, since antibody recognizes specific antigens which are the foreign substances that invade living creatures. Therefore, it can provide optimal solution to external environment. Simulation results by immune based 2-DOF controller reveal that immune algorithm is an effective approach to search for 2-DOF controller.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.1
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• pp.38-52
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• 2016

This paper describes the nonlinear dynamic motion behavior of a ship equipped with a portable dynamic positioning (DP) control system, under external forces. The waves, current, wind, and drifting forces were considered in the calculations. A self-tuning controller based on a neuro-fuzzy algorithm was used to control the rotation speed of the outboard thrusters for the optimal adjustment of the ship position and heading and for path tracking. Time-domain simulations for ship motion with six degrees of freedom with the DP system were performed using the fourth-order RungeeKutta method. The results showed that the path and heading deviations were within acceptable ranges for the control method used. The portable DP system is a practical alternative for ships lacking professional DP facilities.