• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1094-1102
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• 2008

Through the motion control experiment using Industrial Emulator(Model 220 by ECP), the performance comparison of three kinds of controllers such as PID, RIC and LQR was carried out. It was shown that RIC has the best performance in the presence of disturbances such as step one, sinusoidal one and Coulomb friction for the rigid body. LQR using feedback state variables has the best tracking performance far the flexible body. The performance of PID controller is low compared to other controllers, but the design process is simple. The most advanced controller is LQR. In order to attenuate disturbance, an additional state observer should be used to estimate it, making more complex control system. RIC lies between PID and LQR in view of complexity of design. Even though RIC is not complicated, it has good disturbance rejection ability and less tracking error. By considering these aspects, the RIC is suggested as high precision controller to be used in motion control system.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.551-556
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• 2014

This paper proposes a balancing control and driving control of a bicycle robot based on dynamic modeling of the bicycle robot, which has been derived using the Lagrange equations. For the balancing control of the bicycle robot, a reaction wheel pendulum method has been adopted in this research. By using the dynamics equations of the bicycle robot, an LQR controller has been designed for a balancing and driving control of a bicycle robot. The performance of the balance control is verified experimentally before the driving control, which shows a stable posture within one degree vibrations. To show the dynamic characteristics of the bicycle robot during driving, a trapezoidal velocity trajectory is selected as the references. Through simulations and real experiments, the effectiveness of the proposed algorithm has been demonstrated.

• Journal of Korean Association for Spatial Structures
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• v.8 no.4
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• pp.73-80
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• 2008

The objective of current study is to develop an optimization technique for the seismic actively controlled building structures using active tendon devices by an efficient solution of LQR control gain. In order to solve the active control system, the Ricatti closed-loop algorithm has been applied, and the state vector has been formulated by the transfer matrix and solved by a numerical technique of the trapezoidal rule. The time-delay problem has been also considered by phase compensation. To optimize the performance index, the ratio of the weighted matrix is the design variable, allowable story drift limits of IBC 2000 and tendon forces have been applied as restraint conditions, and the optimum control program has been developed with the algorithm of the SUMT technique. In examples of the optimization problem of eight stories shear buildings, it is evaluated that the optimum controlled building is more suitable in the control of earthquake response than the uncontrolled system and can reduce the performance index to compare with the controlled system with a constant ratio of the weighted matrix.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1252-1257
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• 2004

This paper presents a decentralized LQ-PID controller for the TITO system which satisfies the performance of good command following, disturbance rejection, and sensor noise reduction that is design specifications in the frequency domain The procedure is developed by establishing the relationship between the closed-loop state equations including the decentralized PID tuning parameters and the closed-loop state equations of LQR and by selecting the weighting factors Q and R of the cost function in order to satisfy the design specifications in the frequency domain.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1008-1013
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• 2005

In this paper, position tracking control of an autonomous helicopter is presented. Velocity is controlled by using an optimal state controller LQR. A position control loop is added to form a PD controller. To minimize a position tracking error, neural network is introduced. The reference compensation technique as a neural network control structure is used, and a position tracking error of an autonomous helicopter is compensated by neural network installed in the remotely located ground station. Considering time delays between an autonomous helicopter and the ground station, simulation studies have been conducted. Simulation results show that the LQR with neural network compensation performs better than that of the LQR itself.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.3
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• pp.217-225
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• 2013

Typical quadrotor aircraft use four differential thrust vectors to control the motion. In this study, we design a quadrotor aircraft using collective and cyclic control to improve the shortcomings of existing quadrotor aircraft. The quadrotor aircraft with cyclic control can fly at various attitudes due to the excessive control degrees of freedom. Hence the quadrotor aircraft with cyclic control is suitable as high performance aircraft. In this study, modeling and stability analysis of the quadrotor aircraft have been performed using FLIGHTLAB. LQR control systems were designed using linear models at various flight conditions and verified through nonlinear simulations using MATLAB.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1867-1870
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• 1997

Waypoint guidance is a technique used to steer an autonomous vehicle along a desired trajectory. In this paper, a waypoint guidance algorithm for horizontal plane is derived by combining a line following guidance law and a turning guidance law. The line following guidance is derived based on LQR while the turning guidance is designed using rendzvous problem. Through simulation, the proposed method shows a good performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.125-128
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• 2004

Q-learning은 강화학습의 한 방법으로서, 여러 분야에 널리 응용되고 있는 기법이다. 최근에는 Linear Quadratic Regulation (이하 LQR) 문제에 성공적으로 적용된 바 있다. 특히 시스템 모델의 파라미터에 대한 구체적인 정보가 없는 상태에서 적절한 입력과 출력만을 가지고, 학습을 통해 문제를 해결할 수 있어서 상황에 따라서 매우 실용적인 대안이 될 수 있다. 이에 따라 본 논문에서는 이러한 일반적인 LQR Q-learning(이하 LQRQL) 학습방법에 퍼지 모델을 이용하여 제어기를 설계하는 방법을 고려하고, 일반적인 LQROL 기법과 본 논문에서 제시한 방법의 결과를 비교하여 응용 가능성을 살펴보았다.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1201-1207
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• 2010

This paper shows to stabilize a balancing robot. We derive the dynamics of a balancing robot and design its controller using LQR method. For stabilizing balancing robot, we introduce a method to detect an angle using inertial sensors. In this study, we use a complementary filter to fuse signals by frequency response of gyroscope and accelerometer in order to measure the inclined angle of balancing robot. The filter coefficients are obtained by least square to minimize error in angle-detecting filter design. And then, after we derive a dynamics of balancing robot using Lagrange method, we linearize that dynamics for using LQR method.