• Smart Structures and Systems
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• v.23 no.3
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• pp.243-261
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• 2019

This paper introduces an appropriate technique to estimate the weighting matrices used in the linear quadratic regulator (LQR) method for active structural control. For this purpose, a parameter is defined to regulate the relationship between the structural energy and control force. The optimum value of the regulating parameter, is determined for single degree of freedom (SDOF) systems under seismic excitations. In addition, the suggested technique is generalized for multiple degrees of freedom (MDOF) active control systems. Numerical examples demonstrate the robustness of the proposed method for controlled buildings under a wide range of seismic excitations.

• 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.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.109-112
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• 2005

This paper presents an optimal and robust PI-PD controller design method for the second-order systems both with dead time and without dead time to satisfy the design specifications in the time domain via LQR design technique. The optimal tuning method of PI-PD controller are also developed by setpoint weighting and neural networks. It is shown that the simulation results show significantly improved performance by proposed method.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.3
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• pp.132-138
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• 2011

This paper presents an intelligent control approach for lateral position control of an autonomous four wheel steered snowplowing robotic vehicle. The vehicle is built for removing snow on the highway. Dynamics of the vehicle is derived and linearized for LQR control. Lateral position is controlled by the LQR method first, then the neural network control technique is introduced to improve tracking performances under the presence of load. The feasibility of using four wheel steering control is investigated by simulation studies of lateral position tracking of the Ford F-250 truck model. Performances of a LQR control method and a neural network control method under virtual snowplowing situation are compared.

• Journal of Energy Engineering
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• v.13 no.1
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• pp.68-74
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• 2004

This paper presents the design of DSTATCOM (Distribution Static Synchronous Compensator) controller. The results are verified by using PSCAD/EMTDC package. The state equation derived by decomposition analysis of DSTATCOM current component is applied to load model and the combined model which considered constraint condition. In case of single line to ground fault, the conventional method of Pl control is compared with LQR control technique. LQR control is shown to be superior in terms of response profile and composition of voltage sag.

• 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.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.1
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• pp.79-83
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• 2000

Although it is well known that fuzzy learning controller is powerful for nonlinear systems, it is very difficult to apply a learning method if they are unstable. An unstable system diverges for impulse input. This divergence makes it difficult to learn the rules unless we can find the initial rules to make the system table prior to learning. Therefore, we introduced LQR(Linear Quadratic Regulator) technique to stabilize the system. It is a state feedback control to move unstable poles of a linear system to stable ones. But, if the system is nonlinear or complicated to get a liner model, we cannot expect good results with only LQR. In this paper, we propose that the LQR law is derived from a roughly approximated linear model, and next the fuzzy controller is tuned by the adaptive on-line learning with the real nonlinear plant. This hybrid controller of LQR and fuzzy learning was superior to the LQR of a linearized model in unstable nonlinear systems.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.4
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• pp.382-390
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• 2009

The submerged body near the free surface is disturbed by the 1st and 2nd order wave forces, which results in unstable movements when no control is applied. In this paper, the vertical motions of the submerged body are analyzed, and the time-variant nonlinear system for the vertical motions of the submerged body is transformed to the time-invariant linear system in state space. Next, depth controller of the submerged body is designed by using LQR control, one of the modern optimal control technique. Numerical simulation shows that effective depth controls can be achieved by LQR control.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1014-1019
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• 2005

In this paper, a neural network technique for automatic steering control of a four wheel drive autonomous highway snowplow vehicle is presented. Controllers are designed by the LQR method based on the vehicle model. Then, neural network is used as an auxiliary controller to minimize lateral tracking error under the presence of load. Simulation studies of LQR control and neural network control are conducted for the vehicle model under a virtual snowplowing situation. Tracking performances are also compared for two and four wheeled steering vehicles.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.540-547
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• 2009

In the near future, military robots are likely to be substituted for military personnel in the field of battle. The power system of a legged robot is considerably more complex than the one used for a land vehicle because of the coordination and stability issues due to the large number of degree of freedom. In this paper, a servovalve-piston combination system for a straight-line motion of robot leg is modeled as three degree of freedom based on double inputs and single output transfer function. The output is the displacement of piston from neutral. The inputs are valve displacement from neutral and arbitrary load force in this system. LQR(Linear Quadratic Regulator) technique is applied in order to achieve robust stability and fast responses of the system. The Kalman filter loop, rejection of disturbance and noise, riccati equation, filter gain matrix, and frequency domain equality are analyzed and designed.