• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.553-559
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• 1999

The robust control problem of the linear systems with uncertainty is classified as the robust stability problem guaranteeing the stability and the robust performance problem guaranteeing the disired performance. In this paper, we considered the robust performance analysis problem, which find the upper buund of the quadratic performance of the uncertain linear system, and the robust guaranteed performance controller design problem which design a controller guaranteeing the desired quadratic performance. At first, we treated the analysis problem and presented the two results; one is dependent on the performance of the nominal system and another is independent on this. And we treated the design method guaranteeing the desired performance for the uncertain linear systems, Finally, we show the usefulness of our results by numerical examples.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1382-1386
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• 2003

In this paper, we propose a controller for the position of a cart and the angle of a pendulum. To achieve both purposes simultaneously, we divide the system into the dominant subsystem and the dominated one after partial feedback linearization. The proposed controller is composed of a nonlinear controller stabilizing the dominant subsystem and a linear quadratic controller. Using the proposed controller, the controllable region is increased by the nonlinear control part and the control input minimized by the linear control part (LQR).

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.75.5-75
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• 2001

In this paper, we study design of a ILQ(Inverse Linear Quadratic optimal control) looper control system for hot strip mills. The looper which is placed between stands plays an important role in controlling strip width by regulating strip tension variation generated from the velocity difference of main work rolls. A Looper servo controller is designed by ILQ control theory which is an inverse problem of LQ(Linear Quadratic optimal control) control. The mathematical model for looper system is obtained by Taylor´s linearization of nonlinear differential equations. Then we designed linear controller for linearization model by using the ILQ control algorithm. Thereafter this controller is applied to the nonlinear model for model identification. As a result, we show the controller´s robustness for the model error, external disturbance and sensor noise.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.655-660
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• 2009

In many real applications, the discrepancy problem between controller outputs and plant inputs or the abrupt variation problem of controller outputs can occur. These problems have a negative effect on control performance and stability. It is well-known that two phenomena called 'windup' and 'bump' cause these problems. So far these problems have been studied separately in each side of the anti-windup and the bumpless transfer. This paper proposes a new unified method combines the anti-windup and the bumpless transfer method using the linear quadratic minimization and a proper state space model representation for the anti-windup controller. The proposed method has a feature that it takes account of both the anti-windup and the bumpless transfer in one formula. Finally, we exemplify the performance of the proposed method via numerical examples using the controller switching between the anti-windup PID controller and the anti-windup LQ controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.458-465
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• 2008

In this paper, we propose a non-fragile guaranteed cost controller design method for uncertain linear systems with constant delyas in state. The norm bounded and time-varying uncertainties are subjected to system and controller design matrices. A quadratic cost function is considered as the performance measure for the system. Based on the Lyapunov method, an LMI(Linear Matrix Inequality) optimization problem is established to design the controller which uses information of delayed state and minimizes the upper bound of the quadratic cost function for all admissible system uncertainties and controller gain variations. Numerical examples show the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.742-746
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• 1992

This paper proposes a new linear robust state feedback controller for the linear discrete time systems which have uncertainties in the state and input matrices. The uncertainties need not satisfy the matching conditions, but only their bounds are needed to be known. The proposed controller is derived from the linear quadratic game problem, which solution is obtained via the modified algebraic Riccati equation. The controller guarantees the robust performance bound. The bound of the solution and the condition of the uncertainties, which can stabilize the uncertain system are explored.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.40.1-40.1
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• 2011

This paper presents a pitch controller which can hold output power constant at the rated value. Although wind turbine contains complicated nonlinearities, its behaviour within a certain operating range of a point can be approximated by that of a linear model. By doing so, we can apply rather simple and systematic linear control techniques such as PID and LQR(Linear Quadratic Regulator) to design a linear pitch controller. Because these linear controllers are valid only in a sufficiently small range around an operating point, linearized wind turbine model under the condition of varying wind speed needs a linear pitch controller can achieve the aims of tracking the rated rotor rotational speed. We propose an improved linear pitch controller taking each merit of LQR and PI controller under the condition of varying operating points in this paper.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.2
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• pp.118-128
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• 2012

The goal of this paper is to design the target tracking controller for a quadrotor micro UAV using a vision sensor. First of all, the mathematical model of the quadrotor was estimated through the Prediction Error Method(PEM) using experimental input/output flight data, and then the estimated model was validated via the comparison with new experimental flight data. Next, the target tracking controller was designed using LQR(Linear Quadratic Regulator) method based on the estimated model. The relative distance between an object and the quadrotor was obtained by a vision sensor, and the altitude was obtained by a ultra sonic sensor. Finally, the performance of the designed target tracking controller was evaluated through flight tests.

• Journal of Power Electronics
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• v.11 no.4
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• pp.393-400
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• 2011

The main objective of this paper is to control the speed of Nonlinear Hybrid Electric Vehicle (HEV) by controlling the throttle position. Various control techniques such as well known Proportional-Integral-Derivative (PID) controller in conjunction with state feedback controller (SFC) such as Pole Placement Technique (PPT), Observer Based Controller (OBC) and Linear Quadratic Regulator (LQR) Controller are designed. Some Intelligent control techniques e.g. fuzzy logic PD, Fuzzy logic PI along with Adaptive Controller such as Self Organizing Controller (SOC) is also designed. The design objective in this research paper is to provide smooth throttle movement, zero steady-state speed error, and to maintain a Selected Vehicle (SV) speed. A comparative study is carried out in order to identify the superiority of optimal control technique so as to get improved fuel economy, reduced pollution, improved driving safety and reduced manufacturing costs.

• 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.