• Journal of the Korea Institute of Information and Communication Engineering
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• 제12권12호
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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.

• Journal of Institute of Control, Robotics and Systems
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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.

• 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.

• Journal of Institute of Control, Robotics and Systems
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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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 Institute of Electronics and Information Engineers
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• 제51권2호
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• pp.190-196
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• 2014

In this paper, we get state equations based on wheel's rotation, tilt and steering are independent each other in balancing robot. Accordingly, we propose two LQR controllers which are appropriate for rotation and steering control of a balancing robot. And its superiority and appropriateness are demonstrated by a comparison to a PID method. Simulation results verify the possibility of upright balancing, rectilinear motion and position control. Moreover, experimental results show that it guarantees the performance to apply the two LQR controllers to balance the robot.

• Journal of Institute of Control, Robotics and Systems
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• 제11권10호
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• pp.864-869
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• 2005

This paper proposes a new method for LQR controller design. It is unsystematic and difficult to design an LQR controller by trial and error. The proposed method is capable of systematically calculating weighting matrices for desired pole(s) by the pole's moving-range in S-plane and the relational equation between closed-loop pole(s) and weighting matrices. This will provide much-needed functionality to apply LQR controller. The example shows the feasibility of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• 제23권3호
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• pp.268-274
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• 2013

Any new method for controlling a nonlinear system has widely been reported. An inverted pendulum system has typically been used as a target system for demonstrating its usefulness. In this paper, we propose an algorithm to control a flexible joint cart based inverted pendulum system. Two carts are connected with a spring and one is a driving cart and the other is no driving cart with a pole. We here present a system modeling and a good fuzzy logic based control algorithm. We also introduce LQR (Linar Quadratic Regulator) technique for reducing the number of control variables. By using this technique, the number of input variables for a fuzzy logic controller is become only two not six. So the computational complexity is largely reduced. Moreover, a two-input fuzzy logic controller has a control rule table with a skew-symmetric property. And it will lead the design of a single-input fuzzy logic controller. In order to demonstrate the usefulness of the proposed method and prove the superiority of the proposed method, some computer simulations are presented.

• Proceedings of the KIEE Conference
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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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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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.988-991
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• 1996

The purpose of this thesis is to develop methods of designing robust LQR/LQG controllers for time-varying systems with real parametric uncertainties. Controller design that meet desired performance and robust specifications is one of the most important unsolved problems in control engineering. We propose a new framework to solve these problems using Linear Matrix Inequalities (LMls) which have gained much attention in recent years, for their computational tractability and usefulness in control engineering. In Robust LQR case, the formulation of LMI based problem is straightforward and we can say that the obtained solution is the global optimum because the transformed problem is convex. In Robust LQG case, the formulation is difficult because the objective function and constraint are all nonlinear, therefore these are not treatable directly by LMI. We propose a sequential solving method which consist of a block-diagonal approach and a full-block approach. Block-diagonal approach gives a conservative solution and it is used as a initial guess for a full-block approach. In full-block approach two LMIs are solved sequentially in iterative manner. Because this algorithm must be solved iteratively, the obtained solution may not be globally optimal.