• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1105-1110
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• 2008

In this paper, a saturated LQR controller considering control input's saturation for stable linear time-invariant systems with single control input is studied. Based on Lyapunov stability, two linear matrix inequality sufficient existence conditions for this controller are presented. Through numerical simulations for 2DOF vibrating system, it is confirmed that the saturated LQR controller is stable in the presence of control input's saturation and it is also shown that this controller can be applied to vibrating system practically.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.11
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• pp.930-935
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• 2005

In this paper, position tracking control of an autonomous helicopter is presented. Combining an LQR method and a proportional control forms a simple PD control. Since LQR control gains are set for the velocity control of the helicopter, a position tracking error occurs. To minimize a position tracking error, neural network is introduced. Specially, in the frame of the reference compensation technique for teaming neural network compensator, a position tracking error of an autonomous helicopter can be compensated by neural network installed in the remotely located ground station. Considering time delay between an auto-helicopter and the ground station, simulation studies have been conducted. Simulation results show that the LQR with neural network performs better than that of LQR itself.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.280-288
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• 1998

In this paper, a novel relation between the weighting matrix Q in LQR and the eigenstructure of the desired closed-loop system is proposed. Thus, the state feedback gain with the desired eigenstructure in the LQR can be obtained. The proposed scheme is applied to design a simple 3rd-order system and a flight control system design to show the usefulness of the scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2365-2370
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• 2008

In this paper, applications of wavelet neural network controller to position control of nonlinear system are considered. Wavelet neural network is used in the objectives which improve the efficiency of LQR controllers. It is possible to make unstable nonlinear systems stable by using LQR(Linear Quadratic Regulator) technique. And, in order to be adapted to disturbance effectively in this system it uses wavelet neural network controller. Applying this method to the position control of nonlinear system, its usefulness is verified from the results of experiment.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2239-2241
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• 2004

이 논문은 최적 제어 설계방법 중 하나인 LQR 제어기의 과도 상태를 개선하는 방법에 관한 연구이다. 적절한 상태가중행렬과 제어가중행렬을 설정한 후 대수 Riccati 방정식을 풀면 LQR 제어기가 설계된다. 그런데 이 가중행렬은 시행착오 방법을 이용하여 설정하기 때문에 설계된 제어기의 과도 상태를 개선하기 하기가 매우 어렵다. 이러한 문제점을 해결하기 위한 방법으로 closed-loop 근과 가중행렬과의 상관관계를 수학적으로 표현하고, 이를 바탕으로 설계조건을 만족하도록 시스템의 근을 이동시키는 가중행렬을 구하는 방법을 제시한다. 원운동형 도립진자(rotary type inverted pendulum)를 통해 matlab 모의실험으로 그 타당성을 검증한다. 얻어진 결과를 이용하면 원하는 극점을 갖는 LQR 제어기를 체계적으로 설계할 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.275-280
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• 1986

본 연구는 LQR을 Robust하게 설계하는 방법을 다루었다. Unstructured Perturbation에 대응하기 좋으며 쉽게 다룰 수 있는 주파수 응답형 LQR criteria 선정법과, LQR의 변형으로서 Structured Perturbation에 대하여 유효한 Performance Criteria Insensitive Control을 제시하고 효과를 살펴보았다.

• Journal of IKEEE
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• v.5 no.2 s.9
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• pp.146-152
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• 2001

Two control techniques - PI and LQR(Linear Quadratic Regulator) - of DSTATCOM (Distribution Static Synchronous Compensator) for line voltage regulation in distribution system are presented. It is shown that the voltage waveform is improved if the proposed methods are applied in IEEE 13 radial distribution system using PSCAD/EMTDC package in case of single line-to-ground fault. The three cases - without control, with PI control and with LQR control - are compared. The LQR control is shown to be best in respect of response profile and control effort required among them.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.8 s.173
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• pp.34-41
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• 2005

Generally, it is not easy to get a suitable controller for multi variable systems. If the modeling equation of the system can be found, it is possible to get LQR control as an optimal solution. This paper suggests an LQR learning method to design LQR controller without the modeling equation. The proposed algorithm uses the same cost function with error and input energy as LQR is used, and the LQR controller is trained to reduce the function. In this training process, the Jacobian matrix that informs the converging direction of the controller Is used. Jacobian means the relationship of output variations for input variations and can be approximately found by the simple experiments. In the simulations of a hydrofoil catamaran with multi variables, it can be confirmed that the training of LQR controller is possible by using the approximate Jacobian matrix instead of the modeling equation and this controller is not worse than the traditional LQR controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1091-1094
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• 1996

The Object of this study is to find the relations between LQR and eigenstructure assignment regulator. Algorithms for computing weighting matrices are proposed for the case that (i) closed-loop eigenvalues are specified, and (ii) closed-loop gain matrix is given. We also present a new eigenstructure assignment algorithm that minimizes a linear quadratic performance index.

• Journal of the Earthquake Engineering Society of Korea
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• v.6 no.6
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• pp.49-53
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• 2002

This paper provides the systematic procedure to determine the weighting matrices of optimal controllers considering structural energy. Optimal controllers consist of LQR and ILQR. The weighting matrices are needed first in the conventional optimal control design strategy. However, they are in general dependent on the experienced knowledge of control designers. Applying the Lyapunov function to total structural energy and using the condition that its derivative is negative, we can determine the weighting matrices without difficulty. It is proven that the control efficiency with using determined weighting matrices is achieved well for LQR and ILQR controllers.