• 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.

• Journal of Wind Energy
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• v.2 no.1
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• pp.74-81
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• 2011

This paper deals with the application of LQ control to the power curve tracking control of wind turbine. However, two more additional tasks are required to apply the LQR theory to wind turbine control. One is the tracking problem instead of regulation, because the wind turbine is controlled as variable speed and variable pitch. The other is LQ integral control., because the rotor speed should be tightly controlled without any steady state error. Starting from the analysis of wind characteristics, design requirement of a wind turbine control system is defined. A design procedure of LQ tracking with integral control is introduced. The performance of LQ tracking system is analyzed and evaluated by numeric simulation.

• 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 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.

• 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 Power Electronics
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• v.13 no.6
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• pp.1024-1031
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• 2013

This paper addresses linear quadratic regulation (LQR) for variable speed variable pitch wind turbines. Because of the inherent nonlinearity of wind turbines, a set of operating conditions is identified and then a LQR controller is designed for each of the operating points. The feedback controller gains are then interpolated linearly to get a control law for the entire operating region. In addition, the aerodynamic torque and effective wind speed are estimated online to get the gain-scheduling variable for implementing the controller. The potential of this method is verified through simulation with the help of MATLAB/Simulink and GH Bladed. The performance and mechanical load when using LQR are also compared with those obtained when using a PI controller.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.557-572
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• 2004

Control systems are used to limit structural lateral deflections during large external loads such as winds and earthquakes. Most recently, the semi-active control approach has grown in popularity due to inexpensive control devices that consume little power. As a result, recently designed control systems have employed many semi-active control devices for the control of a structure. In the future, it is envisioned that structural control systems will be large-scale systems defined by high actuation and sensor densities. Decentralized control approaches have been used to control large-scale systems that are too complex for a traditional centralized approach, such as linear quadratic regulation (LQR). This paper describes the derivation of energy market-based control (EMBC), a decentralized approach that models the structural control system as a competitive marketplace. The interaction of free-market buyers and sellers result in an optimal allocation of limited control system resources such as control energy. The Kajima-Shizuoka Building and a 20-story benchmark structure are selected as illustrative examples to be used for comparison of the EMBC and centralized LQR approaches.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.03a
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• pp.475-482
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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 controll designers. Applying the Lyapunov function to the total structural energy and using the contrition that its derivative is negative, we can determine the weighting matrices without difficulty. It is proven that the control efficiency is achieved well for LQR and ILQR.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.8
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• pp.71-77
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• 2002

We consider the simultaneous slewing and vibration suppression control problem of an idealized structural model which has a rigid hub with two cantilevered flexible appendages and finite tip masses. The finite clement method(FEM) is used to obtain linear finite dimensional equations of motion for the model. In the linear quadratic regulator(LQR) problem, a simple method is introduced to provide a physically meaningful performance index for space structure models. This method gives us a mathematically minor but physically important modification of the usual energy type performance index. A numerical procedure to solve a time-variant LQR problem with inequality control constraints is presented using the method of particular solutions.

• Nuclear Engineering and Technology
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• v.29 no.1
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• pp.68-77
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• 1997

The reactor power control system is described in the fashion of the order increased LQR system. To obtain the optimal state feedback gain vectors, the weighting matrix of the performance function should be determined. Since the contentional method has some limitations, stochastic searching methods are investigated to optimize the LQR weighting matrix using the modified genetic algorithm combined with the simulated annealing, a new optimizing tool named the hybrid MGA-SA is developed to determine the weighting parameters of the LQR system. This optimizing tool provides a more systematic approach in designing the LQR system. Since it can be easily incorporated with any forms of the cost function, it also provides the great flexibility in the optimization problems.