• 제어로봇시스템학회논문지
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• 제11권11호
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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.

• 제어로봇시스템학회논문지
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• 제4권3호
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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.

• 한국소음진동공학회논문집
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• 제18권11호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.227-233
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• 1996

The Inverted Pendulum has been one of most popular nonlinear dynamic systems for the exploration of control techniques. This paper presents a new linear optimal control techniques and nonlinear neural network learning methods. The multiayered neural networks are used to add nonlinear effects on the linear optimal regulator(LQR). The new regulator can compensate nonlinear system uncertainties that are not considered in the LQR design, and can tolerated a wider range of uncertainties than the LQR alone. The new regulator has two neural networks for modeling and control. The neural network for modeling is used to obtain a more accurate model than the given mathematical equations. The neural network for control is used to overcome deficiencies by adding corrections to the linear coefficients of the LQR and by adding nonlinear effects on the LQR. Computer simulations are performed to show the applicability and a more robust regulator than the LQR alone.

• 한국정밀공학회지
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• 제22권8호
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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.

• 한국공작기계학회논문집
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• 제10권5호
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• pp.78-84
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• 2001

The translation system is made up of springs, masses and a dashpot. This precise piece of equipment is controlled electro-mechanically by a motor and operating program. The control strategy of the system can be changed by spring stiffness, change of mass, and the damping coefficient of the dashpot. This system proves the necessity and effect of a closed loop control. In this paper, PD control experiments were implemented for the translation system. When the north falter was added on the PD controller, we compared the response characteristics of the two systems. The state feedback controller minimized scalar control gains and the resulting response characteristics of the system were studied using the LQR design. Finally, we improved the response characteristics of the translation system which are rising time, settling time, steady state error, and overshoot LQR was better as compared with PD control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2011년도 춘계학술대회 논문집
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• pp.1021-1022
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• 2011

• 전기전자학회논문지
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• 제5권2호
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• pp.146-152
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• 2001

본 논문에서는 배전 계통에서 전압 파형 개선을 위하여 배전용 정지형 보상기(DSTATCOM)의 제어 기법 2가지를 보였다. 1선 지락 사고 시에 제시한 방법을 이용한 시뮬레이션을 통해 선로 전압 파형이 개선됨을 알 수 있다. 또한 비제어 및 PI 제어와 LQR 제어를 비교하였다. 요구되는 응답 특성 및 제어 효과에서 LQR 제어가 우수함을 보이고 PSCAD/EMTDC을 이용하여 검증하였다.

• Smart Structures and Systems
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• 제20권3호
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• pp.273-283
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• 2017

This paper presents and compares three feedback control strategies for active control of noise inside a 3-D vibro-acoustic cavity. These are a) control strategy based on direct output feedback (DOFB) b) control strategy based on linear quadratic regulator (LQR) to reduce structural vibrations and c) LQR control strategy with a weighting scheme based on structural-acoustic coupling coefficients. The first two strategies are indirect control strategies in which noise reduction is achieved through active vibration control (AVC), termed as AVC-DOFB and AVC-LQR respectively. The third direct strategy is based on active structural-acoustic control (ASAC). This strategy is an LQR based optimal control strategy in which the coupling between the various structural and the acoustic modes is used to design the controller. The strategy is termed as ASAC-LQR. A numerical model of a 3-D rectangular box cavity with a flexible plate (glued with piezoelectric patches) and with other five surfaces treated rigid is developed using finite element (FE) method. A single pair of collocated piezoelectric patches is used for sensing the vibrations and applying control forces on the structure. A comparison of frequency response function (FRF) of structural nodal acceleration, acoustic nodal pressure, and piezoelectric actuation voltage is carried out. It is found that the AVC-DOFB control strategy gives equal importance to all the modes. The AVC-LQR control strategy tries to consume the control effort to damp all the structural modes. It is seen that the ASAC-LQR control strategy utilizes the control effort more intelligently by adding higher damping to those structural modes that matter more for reducing the interior noise.