• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.215-218
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• 2002

A control strategy for a dynamic system under irregular disturbance by using stochastic controller is developed. In order to design stochastic controller, system dynamic model in real domain i transformed dynamic moment equation in stochastic domain by F-P-K approach. A study of real time control technique four stochastic controller is performed in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1260-1265
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• 2008

This study deals with modeling and flight control of quadrotor type (QRT) unmanned aerial vehicles (UAVs). Rigorous dynamic model of a QRT UAV is obtained both in reference and body frame coordinate systems. A disturbance observer (DOB) based controller using the derived dynamic models is also proposed for robust hovering control. The control input induced by DOB is helpful to use simple equations of motion satisfying accurate derived dynamics. The experimental results show the performance of the proposed control algorithm.

• Journal of Power System Engineering
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• v.5 no.4
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• pp.90-96
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• 2001

In this study, a robust controller to control pressure in a pneumatic pressure vessel considering dynamic characteristics of pneumatic transmission line is proposed. Dynamic characteristics of transmission line using compressible fluid is changed by the flowing states of the fluid. So, if the fixed gain controller is designed based on a fixed model, the performance of the control system could be destabilized or degraded. The controller designed in this study is composed of two parts. The one is to reject modelling error based on the disturbance observer, the other is to obtain the control performance. The control results with the designed controller show that the robustness of the control system is achieved regardless of the change of the model of the transmission line. Therefore, the designed controller can be utilized for the performance improvement of the pressure control system using compressible fluid such as air and gas

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.113-118
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• 1999

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important n the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.208-208
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• 2000

In realistic control systems, the nonlinear salutation attributes of the control actuator due to physical limitations should be taken into account. This nonlinear saturation of actuators may cause not only deterioration of the control performance but also a large overshoot during start-up and shut-down. As the overshoot increases, the system may become oscillatory unstable. In this paper, the supervisor implementation which guarantees good performance lot saturation operation and prevents reset wind-up is presented. Moreover, the sufficient conditions of the stability for saturated systems using supervisory control with a dynamic controller are provided in the discrete-time domain. A numerical example is illustrated to depict the efficiency of supervisory control for a typical saturated production-distribution system controlled by a discrete-time dynamic controller and to validate basic results by simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.396-400
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• 1996

It is prerequisite to apply dynamics controller to control robot manipulator required to perform fast and Precise motion. In this Paper, we Propose an adaptive 3earning control method for the dynamic control of a robot manipulator. The proposed control scheme is made up of PD controller in the feedback loop and the adaptive learning controller in the feedforward loop. This control scheme has the ability to estimate uncertain dynamic parameters included intrinsically in the system and to achieve the desired performance without the nasty matrix operation. The proposed method is applied to a SCARA robot and experimentally verified.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.715-718
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• 2002

A control strategy for a dynamic system under Irregular disturbance by using stochastic controller is developed. In order to design stochastic controller. system dynamic model in real domain is transformed dynamic moment equation in stochastic domain by F-P-K approach. A study of real time control technique for stochastic controller is presented. The performance of stochastic controller is verified through experiment used by real time control technique method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.630-637
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• 2003

This paper proposes a new modeling scheme to describe the hysteresis and the dynamic characteristics of piezoelectric actuators in the inchworm and develops a control algorithm for the precision motion control. From the analysis of piezoelectric actuator behaviors, the hysteresis can be described by the functions of a maximum input voltage. The dynamic characteristics are also identified by the frequency domain modeling technique based on the experimental data. For the motion control, the hysteresis behavior is compensated by the inverse hysteresis model. The dynamic stiffness of an inchworm is generally low compared to its driving condition, so mechanical vibration may degenerate the motion accuracy of the inchworm. Therefore, the sliding mode control and the Kalman filter are developed for the precision motion control of the inch-warm. To demonstrate the effectiveness of the proposed modeling schemes and control algorithm, experiment validations are performed.

• Journal of Power Electronics
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• v.14 no.5
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• pp.989-999
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• 2014

High-performance Current Source Inverter (CSI)-fed, variable speed alternating current drives are prepared for various industrial applications. CSI-fed Induction Motor (IM) drives are managed by using different control methods. Noteworthy methods include scalar Control (V/f), Input-Output Linearization (IOL) control, Field-Oriented Control (FOC), and Direct Torque Control (DTC). The objective of this work is to compare the dynamic performance of the aforementioned drive control methods for CSI-fed IM drives. The dynamic performance results of the proposed drives are individually analyzed through sensitivity tests. The tests selected for the comparison are step changes in the reference speed and torque of the motor drive. The operation and performance of different vector control methods are verified through simulations with MATLAB/Simulink and experimental results.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.