• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1844-1854
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• 2003

This paper discusses details of modeling and robust control of an AMB (active magnetic bearing) spindle, and part I presents a modeling and validation process of the AMB spindle. There are many components in AMB spindle : electromagnetic actuator, sensor, rotor, power amplifier and digital controller. If each component is carefully modeled and evaluated, the components have tight structured uncertainty bounds and achievable performance of the system increases. However, since some unknown dynamics may exist and the augmented plant could show some discrepancy with the real plant, the validation of the augmented plant is needed through measuring overall frequency responses of the actual plant. In addition, it is necessary to combine several components and identify them with a reduced order model. First, all components of the AMB spindle are carefully modeled and identified based on experimental data, which also render valuable information in quantifying structured uncertainties. Since sensors, power amplifiers and discretization dynamics can be considered as time delay components, such dynamics are combined and identified with a reduced order. Then, frequency responses of the open-loop plant are measured through closed-loop experiments to validate the augmented plant. The whole modeling process gives an accurate nominal model of a low order for the robust control design.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.543-550
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• 2002

This work is concerned with the estimation of output derivatives and their use for the design of robust controller for linear systems with systems uncertainties due to modeling errors and disturbance. It is assumed that a nominal transfer function model and Quantitative bounds for system uncertainties are known. The developed control schemes are shown to achieve regulation of the system output and ensures boundedness of the system states without imposing any structural conditions on system uncertainties and disturbances. Output derivative estimation is first conducted trough restructuring of the plant in a specific parameterization. They are utilized for constructing robust nonlinear high-gain feedback controller of a SMC(Sliding Mode Controller) Type. The performances of the developed controller are evaluated and shown to be effective and useful through simulation study.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.498-503
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• 1993

In this paper, a robust and reliable H$_{\infty}$ control problem is considered for linear uncertain systems with time-varying norm-bounded uncertainty in the state matrix, which performs well despite of actuator outages. Using linear static state feedback and the quadratic stabilization with H$_{\infty}$-norm bound, a robust and reliable H$_{\infty}$ controller is obtained that stabilizes the plant and guarantees an H$_{\infty}$-norm bound constraint on disturbance attenuation for all admissible uncertainties and normal state as well as faulty state of actuators. It provides a sufficient condition for robust and reliable stabilization with H$_{\infty}$-norm bound. Reliability is guaranteed provided actuator outages only occur within a prespecified subset of actuators.tors.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.779-782
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• 1996

Repetitive control is a proposed control strategy in view of the internal model principle and achieves a high accuracy asymptotic tracking property by implementing a model that generates the periodic signals of period into the closed-loop system. Since the repetitive control system contains a periodic signal generator with positive feedback loop, which reduces the stability margin, in the overall closed-loop system, the stability of the closed-loop system should be considered as an important problem. In case that a real system has plant uncertainties which are not represented through modeling, the robust stability problem of the repetitive control system has not been considered sufficiently. In this paper, we propose the robust stability condition for the system with modeling uncertainty. The proposed robust stability condition will be obtained using the robust performance condition in the H$_{\infty}$ control. Moreover, by use of the proposed robust stability condition, we propose a procedure that designs a repetitive controller and a feedback controller simultaneously which can stabilize the overall closed-loop system robustly and which can also do the closedloop system without repetitive controller..

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.275-278
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• 2001

Research on the forced vibration control of a flexible GFR composite beam using $\mu$-synthesis is performed on this paper. Modal analysis method and modal coordinates are introduced to obtain the state equations of the structural system. Using these equations, Robust control algorithm using $\mu$-synthesis is adopted to suppress the forced vibration of a flexible beam since the designed controller can considered plant uncertainty and external disturbance. Constant disturbance which is generated by shaking the flexible beam as I's natural frequency is effectively rejected by a PZT actuator. Simulations and experiments are carried out with the designed controller and effectiveness of forced vibration suppression strategy is verified by results.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.356-362
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• 2002

In this paper, well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. Based on the fuzzy models, a numerical robust stability analysis for the fuzzy feedback linearization regulator is presented using Linear Matrix Inequalities (LMI) Theory. For these structured uncertainty, the closed system can be cast into Lur'e system by simple transformation. From the LMI stability condition for Lur'e system, we can derive the robust stability condition for the fuzzy feedback linearization regulator based on Takagi-Sugeno fuzzy model. The effectiveness of the proposed analysis is illustrated by a simple example.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.199-199
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• 2000

Most of linear time-varying(LTV) systems except special cases have no general solution for the dynamic equations. Thus, it is difficult to design time-varying controllers in analytic ways, and other control design approaches such as robust control have been applied to control design for uncertain LTI systems which are the approximation of LTV systems have been generally used instead. A robust control method such as quantitative feedback theory(QFT) has an advantage of guaranteeing the stability and the performance specification against plant parameter uncertainties in frozen time sense. However, if these methods are applied to the approximated linear time-invariant(LTI) plants which have large uncertainty, the designed control will be constructed in complicated forms and usually not suitable for fast dynamic performance. In this paper, as a method to enhance the fast dynamic performance, the approximated uncertainty of time-varying parameters are reduced by the proposed QFT parameter-scheduling control design based on radial basis function (RBF) networks for LTV systems with bounded time-varying parameters.

• Journal of Electrical Engineering and information Science
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• v.2 no.4
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• pp.28-36
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• 1997

In this paper, robust stability analysis for the fuzzy feedback linearization regulator is presented. Well-known Takagi-Sugeno fuzzy model is used as the MISO nonlinear plant model. Uncertainty and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainty and disturbances, robust stability of the close system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed by using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. The proposed stability analysis is illustrated by a simple example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.158-161
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• 1996

A robust adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs an adaptive fuzzy system to compensate for the uncertainty of the plant. In order to improve the robustness under approximation errors and disturbances, the proposed architecture includes deadzone in adaptation laws. Unlike the previously proposed schemes, the magnitude of approximate errors and disturbances is not required in the determination of the deadzone size, since it is estimated using the adaptation law. The proposed algorithm is proven to be globally stable in the Lyapunov sense, with tracking errors converging to the proposed architecture.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.285-289
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• 1996

Fuzzy logic controllers have been shown better performance than conventional ones especially in highly nonlinear plants. These results are caused by the nonlinear fuzzy rules were not sufficient to cope with significant uncertainty of the plants and environment. Moreover, it is hard to make fuzzy rules consistent and complete. In this paper, we employed a predictive neural network to enhance the nonlinear inference capability. The predictive neural network generates predictive outputs of a controlled plant using the current and past outputs and current inputs. These predictive outputs are used in terms of fuzzy rules in fuzzy inferencing. From experiments, we found that the predictive term of fuzzy rules enhanced the inference capability of the controller. This predictive neural network can also help the controller cope with uncertainty of plants or environment by on-line learning.