• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2345-2348
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• 2003

This paper deals with a problem of automatic landing guidance and control system design. The auto-landing control system for the longitudinal motion is designed in the classical PID controller. The controller gains are properly adapted to variation of the performance using fuzzy logic as a gain scheduler for the PID gains. This control logic is applied to the problem of the automatic landing control system design. From the numerical simulation using the 6DOF nonlinear model of the associated airplane, it is shown that the auto-landing maneuver is successfully achieved from the start of the flight conditions: 1500 ft altitude, 250 ft/sec airspeed and zero flight path angle.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.1
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• pp.28-40
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• 2015

This paper aims to address the intelligent active vibration control problem of a flexible rectangular plate vibration involving parameter variation and external disturbance. An adaptive sliding mode (ASM) MIMO control strategy and smart piezoelectric materials are proposed as a solution, where the controller design can deal with problems of an external disturbance and parametric uncertainty in system. Compared with the current 'classical' control design, the proposed ASM MIMO control strategy design has two advantages. First, unlike existing classical control algorithms, where only low intelligence of the vibration control system is achieved, this paper shows that high intelligent of the vibration control system can be realized by the ASM MIMO control strategy and smart piezoelectric materials. Second, the system performance is improved due to two additional terms obtained in the active vibration control system. Detailed design principle and rigorous stability analysis are provided. Finally, experiments and simulations were used to verify the effectiveness of the proposed strategy using a hardware prototype based on NI instruments, a MATLAB/SIMULINK platform, and smart piezoelectric materials.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.99-104
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• 1991

An adaptive predictive control method for SISO and MIMO plants is proposed. In this method, future predictions of process output based on a bilinear CARIMA model are used to calculate the control input. Also, a classical recursive adaptation algorithm, equation error method, is used to decrease the uncertainty of the process model. As a result of the application on distillation process, the ability of the set-point tracking and the disturbance rejection is acceptable to apply to the industrial distillation processes.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.227-230
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• 1993

Recently, the development of novel control methodology enables us to improve the performance of AC-machine drives by using pulse width modulation (PWM) technique. Usually, the dynamic characteristic of induction motor (IM) has been represented by the 5-th order nonlinear differential equation. This dynamics, however, can be reduced to 3-rd order dynamics by applying direct control of IM input current. This methodology concludes that it is much easier to control IM by means of the field-oriented methods employing the current controller. Therefore a precise current control is crucial to achieve a high control performance both in dynamic and steady state operations. This paper presents an adaptive fuzzy current controller with artificial neural network (ANN) for field-oriented controlled IM. This new control structure is able to adaptively minimize a current ripple while maintaining constant switching frequency. Especially the proposed controller employs neuro-computing philosophy as well as adaptive learning pattern recognizing principles with respect to variations of the system parameters. The proposed approach is applied to the IM drive system, and its performance is tested through various simulations. Simulation results show that the proposed system, compared among several known classical methods, has a superb performance.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.81-90
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• 2000

Parallel manipulator is suitable for the high precise task because it than has higher stiffness, larger load capacity and more excellent precision, due to the closed-lop structure, than serial manipulator. But the controller design for parallel manipulator is difficult because the parallel manipulator has both the complexity of structure and the interference of actuators. The precision improvement of parallel manipulator using a classical linear control scheme is difficult because the parallel manipulator has the tough nonlinear characteristics. In this paper, firstly, the kinematic analysis of a parallel manipulator used at the experiments is performed so as to show the controllability. The analysis of internal singular configuration of the workspace is performed using the kinematic isotropic index so a sto show the limitation of control performance of a simple linear controller with fixed control gains. Secondly, a control scheme is designed by using an adaptive fuzzy logic controller so that active joints of the parallel manipulator track more precisely the desired input trajectory. This adaptive fuzzy logic controller so that active joints of the parallel manipulator track more precisely the desired input trajectory. This adaptive fuzzy logic controller is often used for the control of nonlinear system because it has both the inference ability and the learning ability. Lastly, the effeciency of designed control scheme is demonstrated by the real-time control experiments with IBM PC interface logic H/W and S/W of my won making. The experimental results was a success.

• International Journal of High-Rise Buildings
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• v.8 no.2
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• pp.117-123
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• 2019

Tall buildings are prone to wind-induced vibrations due to their slenderness whereby peak structural accelerations may be higher than the recommended maximum value. The common countermeasure is the installation of a tuned mass damper (TMD) near the highest occupied floor. Due to the extremely large modal mass of tall buildings and because of the narrow to broad band type of wind excitation the TMD mass may become inacceptable large - in extreme cases up to 2000 metric tons. It is therefore a need to develop more efficient TMD concepts which provide the same damping to the building but with reduced mass. The adaptive TMD concept described in this paper represents a solution to this problem. Frequency and damping of the adaptive TMD are controlled in real-time by semi-active oil dampers according to the actual structural acceleration. The resulting enhanced TMD efficiency allows reducing its mass by up to 20% compared to the classical passive TMD. The adaptive TMD system is fully fail-safe thanks to a smart valve system of the semi-active oil dampers. In contrast to active TMD solutions the adaptive TMD is unconditionally stable and its power consumption on the order of 1 kW is negligible small as controllable oil dampers are semi-active devices. The adaptive TMD with reduced mass, stable behavior and lowest power consumption is therefore a preferable and cost saving damping tool for tall buildings.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1409-1423
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• 2018

Compared with classical linear controllers, a nonlinear controller can result in better control performance for the nonlinear uncertainties of continuously variable transmission (CVT) systems that are driven by a synchronous reluctance motor (SynRM). Improved control performance can be seen in the nonlinear uncertainties behavior of CVT systems by using the proposed mingled revised recurrent Hermite polynomial neural network (MRRHPNN) control with mend ant colony optimization (ACO). The MRRHPNN control with mend ACO can carry out the overlooker control system, reformed recurrent Hermite polynomial neural network (RRHPNN) control with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, to help improve convergence and to obtain better learning performance, the mend ACO is utilized for adjusting the two varied learning rates of the two parameters in the RRHPNN. Finally, comparative examples are illustrated by experimental results to confirm that the proposed control system can achieve better control performance.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.17 no.4
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• pp.238-266
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• 2013

The Interaction Picture (IP) method is a valuable alternative to Split-step methods for solving certain types of partial differential equations such as the nonlinear Schr$\ddot{o}$dinger equation or the Gross-Pitaevskii equation. Although very similar to the Symmetric Split-step (SS) method in its inner computational structure, the IP method results from a change of unknown and therefore do not involve approximation such as the one resulting from the use of a splitting formula. In its standard form the IP method such as the SS method is used in conjunction with the classical 4th order Runge-Kutta (RK) scheme. However it appears to be relevant to look for RK scheme of higher order so as to improve the accuracy of the IP method. In this paper we investigate 5th order Embedded Runge-Kutta schemes suited to be used in conjunction with the IP method and designed to deliver a local error estimation for adaptive step size control.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.565-568
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• 2010

A pressure control law to regulate mass flow rate of gas generator is suggested. The governing equation is modeled by considering the burning rate of solid propellant and the conservation equation of gas generator. And then, a classical control law is applied after verifying the accuracy of dynamic model through comparing with ground test and internal ballistic results. The results show degradation of performance as shown in typical time varying system. To overcome this problem, an adaptive scheme is suggested and the performance is verified through numerical simulation.