• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2490-2492
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• 2002

Design of controller of nonlinear systems is an important part of control research. In this paper, a controller for nonlinear plants using a neural network is presented. The controller is a combination of an approximate PID controller and a neural network controller. The PID controller be used for stabilizing the process and for compensating for possible disturbances, a neural network act as feedforward controller. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system and meets the demands of the system.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.127-137
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• 2000

This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.3
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• pp.9-13
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• 2007

This Paper deals with the synthesis of absolutely stabilizing fixed order controllers for Lure-Postnikov systems. Lure-Postnikov systems are frequently encountered in mechanical engineering applications. Analytical tools for synthesizing stabilizing fixed structure controllers, such as the PID controllers examining the absolute stability of Lure-Postnikov systems, have recently been studied in the literature. However, tools for synthesizing controllers of arbitrary order have not been studied yet. We propose a systematic method for synthesizing absolutely stabilizing controllers of arbitrary order for the Lure-Postnikov systems. Our approach is based on recent results in the literature on approximation of the set of stabilizing controller parameters that render a family of real and complex polynomials Hurwitz. We provide an example of a robotic system to illustrate the procedure developed.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.10-12
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• 2004

Recently, Datta at al. [1] have developed a metDattad of obtaining the complete set of stabilizing PI, PID controllers for a given LTI system, in which tDattase are determined by solving a set of linear inequalities parameterized by the proportional(P) gain. In this paper, we provide a note about Dattaw Datta's idea can be extended to the problem of finding all stabilizing PD controllers and about an improved metDattad that allows us to calculate the admissible range of P gain more rigorously. An illustrative example is given.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.980-986
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• 1999

In this paper, the optimal controller which decouples a coupled multivariable system and minimizes a quadratic performance index is proposed. Design of the controller is based on the two-degree-of-freedom standard model. The class of all stabilizing and decoupling controllers is parametrized first and the $H^{2}$optimal controller is obtained by using this parametrized form. An illustrative example for a $2{\times}2$ plant is given.

• Journal of National Security and Military Science
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• s.1
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• pp.311-343
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• 2003

In this paper, we propose a generalized disturbance torque suppression control scheme of servo motors for missile fin actuators. Our controller consists of both a model based feed-forward controller and a stabilizing feedback controller. The feed-forward controller is designed such that the output of nominal plant tracks perfectly the reference position command with a desired dynamic characteristics. The feedback controller stabilizes the overall closed loop system. Furthermore, the feedback controller contains a free function that can be chosen arbitrary. The free function can be designed so as to achieve both the suppression of disturbances and the robustness to model uncertainties. In order to illuminate the superior performance of our control scheme to the conventional ones, we present some simulation results.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.363-369
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• 1999

In this paper, we design the robust $H_{\infty}$ controller for water level control of steam generator using a mixed $H_{\infty}$ optimization with model-matching method. Firstly we choose the desired model which has good disturbance rejection performance. Secondly we design a stabilizing controller to keep the model-matching error small and also provide sufficiently large stability margin against additive perturbations of the nominal plant. Simulation results show that proposed robust $H_{\infty}$ controller at specific power operation has satisfactory performances against the variations of load power, steam flow rate, primary circuit coolant temperature, and feedwater temperature. It can be also observed that the proposed robust $H_{\infty}$ controller exhibits better robust stability than conventional PI controller.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.240-245
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• 2010

The controller of a model reference adaptive control monitors the plant's inputs and outputs to acknowledge its characteristics. It then adapts itself to the characteristics it encounters instead of behaving in a fixed manner. An important part of every adaptive scheme is the adaptive law for estimating the unknown parameters on line. A more precise model is required to improve performance and to stabilize a given dynamic system, such as a satellite in which performance varies over time and the coefficients change due to disturbances, etc. After model identification, the robust controller ($H{\infty}$) is designed to stabilize the rigid body and flexible body of a satellite, which can be perturbed due to disturbance. The result obtained by the $H{\infty}$ controller is compared with that of the proportional and integration controller which is commonly used for stabilizing a satellite.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.7-10
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• 1998

Because of simple control algorithm and easy implementation, the conventional PI controller has been widely used in industrial application. But, it is very difficult to find the optimal PI control gain. Therefore, in improperly tuned PI controller or parameter variation, to obtain optimal performance, the novel PI controller, which consist of conventional PI controller and 4-rule based fuzzy logic, are presented in this paper. The novel PI controller which exhibits a stabilizing effects on the closed-loop system, has good robustness regarding the improperly tuned PI controller or parameter variation. The simulations are performed to verify the capability of proposed control method on induction motor.

• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.3
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• pp.8-13
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• 2011

In the present study, a robust controller has been designed to control force for a pneumatic driving system considering the effect of a transmission line. Transfer characteristics of pneumatic transmission line should be changed according to the velocity of the air going through the transmission line. The designed controller is composed of two parts. The one is a feedback controller, which is composed of a stabilizing filter, a compensating filter of modelling error and a nominal model of the force control system, to compensate the influence of transmission line and improve the feedback characteristics of the control system, and, the other is a feedforward controller to achieve the control performance. Control results with the designed controller show that the robustness and performance of the control system are improved compared to the control results with a fixed gain controller.