• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.187-187
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• 1999

in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.7
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• pp.997-1004
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• 1990

In this paper, a design method of a robust adaptive regulator with feedfoward path based on the explicit criterion minimization is proposed. The convergence speed of parameter estimation is improved by using the stochastic Newton minimization method in the criterion minimization algorithm, and sensitivity derivatives are used in the regulator calculation for improving the robustness of the control system. Trh proposed adaptive regulator is applied to the stable minimum-phase and nonminimum-phase system, the results are shown that control performance and disturbance compensation ability of the regulator are improved. And the choosing method of input penalty is proposed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.3
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• pp.85-94
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• 2000

In this paper, a robust H$\infty$ optimal tuning problem of a structure-specified PSS is investigated for power systems with parameter variation and disturbance uncertainties. Genetic algorithm is employed for optimization method of PSS parameters. The objective function of the optimization problem is the H$\infty$-norm of a closed loop system. The constraint of the optimization problem are based on the stability of the controller, limits on the values of the parameters and the desired damping of the dominant oscillation mode. It is shown that the proposed H$\infty$ PSS tuned using genetic algorithm is more robust than conventional PSS.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2455-2457
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• 2000

In this paper, it is proposed a neural adaptve control algorithm for boiler-turbine system. Control inputs are constructed using RBF Neural networks and variable structure inputs are added to improve the robustness. This proposed algorithm does not need the information about parameters and can assure the robustness under the output disturbance and parameter perturbations. The results of computer simulations is presented to verify the efficiency of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2584-2586
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• 2000

A drum level control is one of the most important control systems in thermal power plant. The control objective of drum level of boiler in thermal power plant is to maintain drum level at constant set-point regardless of disturbance such as main steam flow. The implemented drum level controller is the cascade PI controller. The important factor in drum level controller is the parameters of two PI controllers. The tuning of PI controller parameter is tedious and time-consuming job. In this paper, the relay feedback Ziegler - Nichols tuning method extended to auto-tune cascade PI drum level controller. Finally, the simulation result using boiler model in Power Plant shows the validity of auto-tuned cascade PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.151-156
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• 1998

In this paper, we present new control method for robot manipulators. The design objective can be the implementation of minimax controller with H$_{\infty}$ performance via LMI approach to guarantee the robustness and to obtain the exact tracking performance for robot manipulators with system parameter uncertainty and exogenous disturbance. We show that the Algebraic Riccati equation (ARE) which is needed for the construction of H$_{\infty}$ controller can be recast into the Algebraic Riccati Inequality (ARI) and the optimal control gain can be obtained by convex optimization method. Then, we will apply the proposed controller to rigid robot manipulators for verifying the performance of our controller.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.330-336
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• 2005

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inputs. It is shown that all the error signals in the learning system are bounded and the repetitive robot motion converges to the desired one exponentially fast with guaranteed convergence rate. An engineering workstation based control system is built to verify the effectiveness of the proposed control scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.178-186
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• 1998

In this paper, a robust.adaptive learning control scheme is presented for precise trajectory tracking of rigid mobile robots. In the proposed controller, a set of desired trajectories is defined and used in constructing the control input and learning rules which constitute the main part of the proposed controller. Stable operating characteristics such as precise trajectory tracking, parameter estimation, disturbance suppression, etc., are shown thorugh experiments and computer simulations.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.3
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• pp.92-99
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• 2001

This paper presents a simulation study using a robust controller to improve the roll stability of a vehicle. The controller is designed in the framework of an output feedback H$_{\infty}$ control scheme based on the 3DOF linear vehicle model, solving the mixed-sensitivity problem to guarantee the robust stability and disturbance rejection with respect to parameter variations due to laden and running vehicle conditions. In order to investigate the feasibility of the active roll control system in a real car, its performance is evaluated by simulation in a 10DOF full vehicle model with actuator dynamics and tire characteristics.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.8
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• pp.395-401
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• 2005

This paper presents Hybrid PI(HBPI) controller of induction motor drive using fuzzy control. In general, PI controllers used in computer numerically controlled machines process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, HBPI controller proposes a new method based self tuning PI controller. HBPI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of induction motor are presented to show the effectiveness of the proposed gam tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.