• Journal of IKEEE
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• v.23 no.4
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• pp.1353-1359
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• 2019

It is presented in this paper that the static output feedback (SOF) pole-assignment problem of some linear time-invariant systems can be completely resolved by parametrization in real Grassmann space. For the real Grassmannian parametrization, the so-called Plucker matrix is utilized as a linear matrix formula formulated from the SOF variable's coefficients of a characteristic polynomial constrained in Grassmann space. It is found that the exact SOF pole assignability is determined by the linear independency of columns of Plucker sub-matrix and by full-rank of that sub-matrix. It is also presented that previous diverse pole-assignment methods and various computation algorithms of the real SOF gains for 2-input, 2-output, 4^th order systems are unified in a deterministic way within this real Grassmannian parametrization method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.747-752
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• 2004

This paper presents an iterative linear matrix inequality (LMI) approach to the design of a static output feedback (SOF) stabilization controller. A linear penalty function is incorporated into the objective function for the non-convex rank constraint so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. Hence, the overall procedure results in solving a series of semidefinite programs (SDPs). With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Extensive numerical experiments are Deformed to illustrate the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.111-114
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• 2003

In this paper, we consider the stabilization and $H_\infty$ control of linear systems with static output feedback control. The static output feedback control represents the simplest closed-loop control that can be realized in practice, and, moreover, it is less expensive to be implemented and is more reliable. In spite of its advantages, it is one of the open problems which is not sloved analytically or numerically yet. After decompose the closed-loop system into feedback form, by adopting the small gain theorem, we obtain a sufficient condition for stabilization and a sufficient condition for It control expressed as linear matrix inequalites. Finally, we show the usefulness of our results by a numerical example.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2564-2566
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• 2005

This paper presents an iterative linear matrix inequality (LMI) method for $H_2$ and $H_{\infty}$ optimal static output feedback (SOF) control, which is expressed in terms of LMIs subject to an additional rank condition. We propose a linear Penalty function to penalize the rank constraint so that static $H_2$ and $H_{\infty}$ synthesis results in solving a series of convex LMI optimization problems. Numerical experiments for various $H_2$ and $H_{\infty}$ SOF synthesis were performed to demonstrate the effectiveness of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1048-1052
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• 2007

This paper presents a rank-constrained linear matrix inequality(LMI) approach to simultaneous linear-quadratic(LQ) optimal control by static output feedback. Simultaneous LQ optimal control is formulated as an LMI optimization problem with a nonconvex rank condition. An iterative penalty method recently developed is applied to solve this rank-constrained LMI optimization problem. Numerical experiments are performed to illustrate the proposed method, and the results are compared with those of previous work.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.828-834
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• 2014

This paper presents a comparison among three types of approaches to an ARC (Active Roll Control) with an AARB(Active Anti-Roll Bar) for a vehicle system. Lateral acceleration and road profile are considered as disturbance. The ARC is designed with an LQ SOF (Linear Quadratic Static Output Feedback) control, $H_{\infty}$ control and SMC (Sliding Mode Control). These approaches are compared in terms of rollover prevention and ride comfort. For comparison, Bode plot analysis based on linear model and frequency response analysis based on CarSim simulation are performed.

• 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.