• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2155-2157
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• 2002

In this paper, we present a stabilizing controller for the singularly perturbed discrete time bilinear systems. The proposed control method guarantees the robust stability for the resulting closed loop system with multi-input. We verify the proposed algorithm by a numerical example.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.2
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• pp.226-233
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• 1999

A transmitting antenna mounted on the naval vessels can be easily exited by exogenous disturbances such as wave and impact. Gimbal system need for the controller to maintain the robust performance against various modeling uncertainties and disturbances. PI controller, however, cannot supply good robust performance under situation. Thus a robust $H_{\infty}$ control scheme is used to ensure a specified dynamic response under above conditions. Gimbal system controlled simplified as 2 DOF system that ignored coordinate co-relations of each direction and hydraulic system is linearly modelled. In this paper, we compared those of simulation to the results of experiment and H$_{\infty}$ controller, proposed, showed the good response and stability than PI controller.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.223-226
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• 1998

The control objective of steam generator water level in the secondary circuit of a nuclear power plant is to regulate the water level at the desired set point. The dynamics of steam generators is non-linear in nature. The task of modelling such plant is very difficult and especially so when plant operating conditions change frequently. In these reasons, conventional PI gains over all pover range will not work efficiently and a manual control is generally used in low power operation. Therefore the robust H$\infty$ controller design method should be required. In this paper, we design the robust H$\infty$ controller for water level control of steam generator using a mixed H$\infty$ optimization with model-matching method. Firstly we choose the desired model that has good disturbance rejection performance. Secondly we design a stabilizing controller to keep the model-matching error small and also provide sufficiently large stability margin against additive perturbations of the nominal plant.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.839-848
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• 2012

In this paper, we propose a robust control scheme of AC servo motors to suppress disturbance torques effectively. The proposed controller consists of both a model based feed-forward controller and a stabilizing feedback controller. The feed-forward controller is designed such that the output of the nominal plant tracks perfectly the reference velocity command with desired dynamic characteristics. The feedback controller stabilizes the overall closed loop system. Furthermore, the feedback controller contains a free function that can be chosen arbitrarily. The free function can be designed so as to achieve both suppression of disturbances and robustness to model uncertainties. In order to illuminate the superior performance of the proposed control scheme to the conventional ones, we present some simulation results.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.6
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• pp.888-893
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• 1998

In this paper, we present a magnetic levitation system which has not a gap sensor with sensor-less realization and stabilizing controller design. For measuring gap between magnet and levitated object we propose a gap sensorless method and adop two-degree-of-freedom controller for robust-ness and performence of the magnetic levitation system. From time responeses we confirm that the proposed sensorless method which can be applied to magnetic levitation system. Also the designed stabilizing controller has good disturbance rejection and reference tracking performance.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.240-245
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• 2010

The controller of a model reference adaptive control monitors the plant's inputs and outputs to acknowledge its characteristics. It then adapts itself to the characteristics it encounters instead of behaving in a fixed manner. An important part of every adaptive scheme is the adaptive law for estimating the unknown parameters on line. A more precise model is required to improve performance and to stabilize a given dynamic system, such as a satellite in which performance varies over time and the coefficients change due to disturbances, etc. After model identification, the robust controller ($H{\infty}$) is designed to stabilize the rigid body and flexible body of a satellite, which can be perturbed due to disturbance. The result obtained by the $H{\infty}$ controller is compared with that of the proportional and integration controller which is commonly used for stabilizing a satellite.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.535-535
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• 2000

This paper presents an adaptive receding horizon H$_{\infty}$ controller for the linear parameter varying systems in the deterministic environment, which combines a parameter range estimator and a robust receding horizon H$_{\infty}$ controller using the parameter bounds. Using parameter set inclusion and terminal inequality condition, the closed-loop system stability is guaranteed. It is shown that the stabilizing adaptive receding horizon H$_{\infty}$ controller guarantees the H$_{\infty}$ norm bound.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.92-95
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• 1999

The design problem of the control system is the ability to synthesize controller that achieve robust stability and robust performance. The paper explains the Finite Inclusions Theorem (FIT) by the procedure namely FIT synthesis. It is developed for synthesizing robustly stabilizing controller for parametrically uncertain system. The fundamental problem in the study of parametrically uncertain system is to determine whether or not all the polynomials in a given family of characteristic polynomials is Hurwitz i.e., all their roots lie in the open left-half plane. By FIT it can prove a polynomial is Hurwitz from only approximate knowledge of the polynomial's phase at finitely many points along the imaginary axis. An example shows the simplicity of using the FIT synthesis to directly search for robust controller of parametrically uncertain system by way of solving a sequence of systems of linear inequalities. The systems of inequalities are solved via the projection method which is an elegantly simple technique fur solving (finite or infinite) systems of convex inequalities in an arbitrary Hilbert space. Results from example show that the controller synthesized by FIT synthesis is better than by H$\sub$$\infty$/ synthesis with parametrically uncertain system as well as satisfied the objectives for a considerably larger range of uncertainty.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.297-305
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• 1996

In this paper a novel robust adaptive fuzzy controller for the nonlinear system with state-dependent uncertainty is proposed. The conventional adaptive fuzzy controller determines the function of state variable bounding the state-dependent uncertain term in the system dynamics on the local state space by off-line calculation. Whereas the proposed method determines that function by the fuzzy inference so that it guarantees the stability of the closed loop system globally on the whole state space. In addition, the method is applicable to the multi-input system. We applied the proposed method to the Burn Control of the Tokamak fusion reactor whose dynamics contains the state-dependent uncertainty and proved the effectiveness of the scheme by using the simulation results.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.135-142
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• 2008

This paper deals with the design of a robust PI controller for a vehicle suspension system. A method, which is related to computation of all stabilizing PI controllers, is applied to the vehicle suspension system in order to obtain optimum control between passenger comfort and driving performance. The PI controller parameters are calculated by plotting the stability boundary locus in the $(k_p,\;k_i)$-plane and illustrative results are presented. In reality, like all physical systems, the vehicle suspension system parameters contain uncertainty. Thus, the proposed method is also used to compute all the parameters of a PI controller that stabilize a vehicle suspension system with uncertain parameters.