• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.431-438
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• 2000

The cost function consists of the weighting functions concerning the structural responses to be controlled and the controller capability. Therefore, the control efficiency depends on the characteristics of the weighting functions. The objective of this paper is the comparative study of the time domain control strategies of LQR and LQG and the frequency domain strategy of H₂ by setting the equivalent weighting functions to the all control strategies. As a result of analysis, LQR strategy is found to be more efficient than other strategies in terms of the response reduction. but the control force is found to be a little highter. As LQG can compensate the limitation of LQR that all state variables should be identified, LQG is more acceptable algorithm than LQR. Furthermore LQG shows a good performance both in the response reduction and the control force. Finally H₂ algorithm is employed to illustrate the importance of weighting filters considering the frequency characteristics of the response and the controller. It Is shown that the H₂ algorithm is found to be the most effective one for the response control with a little control force having a low frequency band.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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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 Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.856-860
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• 2002

The guaranteed stability margin of LQ optimal regulators with cross-product terms in a performance index is derived for the discrete-time case. In order to obtain the guaranteed stability margin, the singular value of the return difference matrix is examined. A numerical simulation is provided to demonstrate the validity of the derived stability margin.

• 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 Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.701-705
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• 2014

This article presents a comparison among several yaw and roll motion controllers for vehicle rollover prevention. In the previous research, yaw and roll motion controllers can be independently designed for rollover prevention. Following this idea, several yaw and roll motion controllers are designed and compared in terms of rollover prevention. For the yaw motion control, PID, LQR, SMC (Sliding Mode Control) and TDC (Time-Delay Control) are adopted. For the roll motion control, LQR, LQ SOF (Static Output Feedback) control, PID, and SMC are adopted. To compare the performance of each controller, simulation is performed on a vehicle simulation package, CarSim$^{(R)}$. From simulation, TDC and LQ SOF are the best for yaw and roll motion control, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1080-1085
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• 2005

A hybrid control scheme is proposed for the stabilization of backward movement along simple paths for a vehicle composed of a truck and six trailers. The hybrid comprises the combination of a linear quadratic regulator (LQR) and a neurocontroller (NC) that is trained by a genetic algorithm (GA). Acting singly, either the NC or the LQR are unable to perform satisfactorily over the entire range of the operation required, but the proposed hybrid is shown to be capable of providing good overall system performance. The evaluation function of the NC in the hybrid design has been modified from the conventional type to incorporate both the squared errors and the running steps errors. The reverse movement of the trailer-truck system can be modeled as an unstable nonlinear system, with the control problem focusing on the steering angle. Achieving good backward movement is difficult because of the restraints of physical angular limitations. Due to these constraints the system is impossible to globally stabilize with standard smooth control techniques, since some initial states necessarily lead to jack-knife locks. This paper demonstrates that a hybrid of neural networks and LQR can be used effectively for the control of nonlinear dynamical systems. Results from simulated trials are reported.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.57.1-57
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• 2001

The objectives of congestion control in ATM (Asynchronous Transfer Mode) networks are maximum utilization of network resources, acceptable level of low cell loss and fairness among all VCs (Virtual Connections). In this paper, we present a congestion control algorithm which is based on state space model, The proposed controller uses optimal control algorithms (LQR, Mixed-LQR), where control parameters can be designed to ensure the stability of the control loop in a control theoretic sense, over the propagation delay. We show how the control mechanism can be used to design a controller to support ABR service based on feedback of explicit rates. Simulation results are presented to substantiate our claim.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.84-91
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• 2006

The synthesis of optimal controllers for multivariable systems usually requires an accurate linear model of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. This paper presents a novel loaming method for the synthesis of LQR controllers that doesn't require explicit modeling of the plant dynamics. This method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the LQR objective function. It becomes easier and more convenient because it is relatively very easy to get the sign of Jacobian instead of its Jacobian. Simulations involving an overhead crane and a hydrofoil catamaran show that the proposed LQR-LC algorithm improves controller performance, even when the Jacobian information is estimated from input-output data.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.183-186
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• 2007

This Paper presents a solution to control the Brushless DC Motor(BLDCM) in Hard Disk Drive(HDD) using the Linear Quadratic Regulator(LQR). Generally, The speed of BLDCM in HDD is controlled by the lead angle control or the input voltage control using PAM(Pulse Amplitude Modulation) etc. These control methods have speed overshoot in speed control and need the long time until BLDCM reaches at the steady state. In order to improve the performance, this paper present the PI speed controller using the LQR based on vector control and the rotor position detection methods at the space vector PWM inverter. The proposed methods are proved by the simulation and experimental results.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.1
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.