• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.15-18
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• 2007

We consider the problem of designing a static output feedback sliding mode control law for linear dynamical systems with mismatched uncertainties in the state matrix. We assume that an output dependent sliding surface guaranteeing the quadratic stability of the sliding mode dynamics is given, the reachability condition is not required to be satisfied globally, and the output feedback sliding mode control law complises both linear and discontinuous parts. We reduce the problem of designing the linear part of the sliding mode control law to a simple LMI problem which offers design flexibility for combining various useful convex design specifications. Our approach does not require state transformation and it can be applied to mismatched uncertain systems.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.2
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• pp.29-36
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• 2004

This paper deals with a fractional model reduction for linear systems with time varying delayed states. A contractive coprime factorization of linear time delayed systems is defined and obtained by solving linear matrix inequalities. Using generalize controllability and observability gramians of tile contractive coprime factor, a balanced state space realization of the system is derived. The reduced model will be obtained by truncating states in the balanced realization and an upper bound of model approximation error is also presented. In order to demonstrate efficacy of the suggested method, a numerical example is illustrated.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.295-303
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• 2001

The demand for steam generators is increasing in industrial systems in which the design strategy should be implemented for safe and efficient operation of steam generators. It is, however, difficult to design a controller by the conventional method because of the nonlinear dynamics of the steam generator and influences by the set value of disturbance. This paper presents an automatic parameter optimization technique for a multivariable fuzzy controller using evolutionary strategy, At first, we use the steady state information such as a steady state gain matrix(SSGM) and a relative gain matrix(RGM). We can obtain much information on the control inputs and the outputs of the boiler-turbine system from the matrices. In order to determine the structure of the controller by using RGM and SSGM, the fuzzy rules are trained by evolutionary strategy. The good performance of the proposed multivariable fuzzy controller is verified through simulations.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.217-219
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• 2001

This paper presents a new eigenvalue sensitivity analysis method based on AESOPS algorithm. The additional calculation steps are derived from the original AESOPS algorithm. The additional calculation steps are performed directly from the AESOPS algorithm after iteratively calculating electro-mechanical oscillation modes in small signal stability problems. Owing to the structural characteristics of partitioned sub-matrix of state space equations, the partial differentiation terms of system state matrix for obtaining eigenvalue sensitivity indices can be calculated very simply. By the method presented in this paper, the AESOPS algorithm can be used in controller design problem as well as analysis of small signal stability problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.10 s.253
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• pp.1249-1254
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• 2006

Existing adaptive observers may cause the parameter drifts due to disturbances even if state estimation errors remain small. To avoid the drift phenomena in the presence of bounded disturbances, several robust adaptive observers have been introduced addressing bounds in state and parameter estimates. However, it is not easy for these observers to manipulate the size of the bounds with the selection of the observer gain. In order to reduce estimation errors, this paper introduces the (equation omitted) gain minimization problem in the adaptive observer structure, which minimizes the (equation omitted) gain between disturbances and estimation errors. The stability condition of the adaptive observer is reformulated as a linear matrix inequality, and the observer gain is optimally chosen by solving the convex optimization problem. The estimation performance is demonstrated through a numerical example.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.263-268
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• 2008

An improved linear matrix inequality (LMI) representation of delay-independent $H_2$ performance analysis is introduced for linear delay systems with delays of any size. Based on this representation we propose a new $H_2$ memoryless state feedback design. By introducing a new matrix variable, the new LMI formulation enables us to parameterize memoryles s controllers without involving the Lyapunov variables in the formulations. By using a parameter-dependent Lyapunov function, this new representation proposed here provides us the results with less conservatism.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.119-121
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• 2003

In small signal stability analysis of power systems, eigenvalue analysis is the most useful method and the detailed modeling of generator gives an important effect to the eigenvalues. Generator full model is used for precise dynamic analysis of generators and controllers while two-axis model is used for multimachine systems because of the reduced order of the state matrix. Also, the eigenvalue sensitivity coefficients are used for optimization of controller parameters to improve system stability. This paper compare the first order eigenvalue sensitivity coefficients of controllers in case of generator full model with those of two-axis model. As a result of an example the estimated eigenvalues using sensitivity coefficients in case of generator full model is very close to those of state matrix within 1% error ratios.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.61-76
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• 2002

The robust non-fragile guaranteed cost control problem is studied in this paper for class of uncertain linear large-scale systems with time-varying delays in subsystem interconnections and given quadratic cost functions. The uncertainty in the system is assumed to be norm-hounded arid time-varying. Also, the state-feedback gains for subsystems of the large-scale system are assumed to have norm-bounded controller gain variations. The problem is to design state feedback control laws such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound far all admissible uncertainties. Sufficient conditions for the existence of such controllers are derived based on the linear matrix inequality (LMI) approach combined with the Lyapunov method. A parameterized characterization of the robust non-fragile guaranteed cost contrellers is 7iven in terms of the feasible solution to a certain LMI. Finally, in order to show the application of the proposed method, a numerical example is included.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.524-529
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• 2006

In this paper, we propose a method to design variable structure systems with sliding sector for multi-input multi-output systems with mismatched uncertainties in the state matrix. For the uncertain systems we define sliding sectors within which a norm of the state decreases with zero input despite of mismatched uncertainties. Using the notion of the sliding sector we give simple design algorithms of variable structure control laws that can reduce the chattering. Finally, we give a design example in order to show the effectiveness of our method.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.144-149
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• 2006

This paper deals with a balanced model reduction for uncertain nonlinear systems via T-S fuzzy approach. We define a generalized controllability/observability gramian and obtain a balanced state space model using generalized gramians which can be obtained from solutions of linear matrix inequalities. We present a balanced model reduction scheme by truncating not only state variables but also uncertain elements. An upper bound of the model reduction error will also be suggested. In order to demonstrate the efficacy of our method, a numerical example will be presented.