• Journal of Power Electronics
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• v.19 no.2
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• pp.569-579
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• 2019

A robust controller is designed based on feedback linearization and sliding mode control to damp sub-synchronous control interaction (SSCI) in doubly fed induction generator (DFIG) wind power plants (WPPs) interfaced with the grid. A feedback-linearized sliding mode controller (FLSMC) is developed for the rotor-side converter (RSC) through feedback linearization, design of the sliding mode controller, and parameter tuning with the use of particle swarm optimization. A series-compensated 100-MW DFIG WPP is adopted in simulation to evaluate the effectiveness of the designed FLSMC at different compensation degrees and wind speeds. The performance of the designed controller in damping SSCI is compared with proportional-integral controller and conventional sub-synchronous resonance damping controller. Besides the better damping capability, the proposed FLSMC enhances robustness of the system under parameter variations.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.364-371
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• 2007

In this paper, we describe the synthesis of robust and non-fragile $H^{\infty}$ state feedback controllers for linear systems with affine parameter uncertainties, as well as a static state feedback controller with poly topic uncertainty. The sufficient condition of controller existence, the design method of robust and non-fragile $H^{\infty}$ static state feedback controller with fuzzy compensator, and the region of controllers that satisfies non-fragility are presented. We show that the resulting controller guarantees the asymptotic stability and disturbance attenuation of the closed loop system in spite of controller gain variations within a resulted polytopic region.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.341-344
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• 2007

This paper proposes an alternative robust adaptive high-gain fuzzy observer design scheme and its application to synchronization of chaotic systems. The structure of the proposed observer is represented by Takagi-Sugeno fuzzy model and has the integrator of the estimation error. This improves the performance of high-gain observer and makes the proposed observer robust against noisy measurements, uncertainties and parameter perturbations as well. Using Lyapunov stability theory, an adaptive law is derived and the stability of the proposed observer is analyzed. Some simulation result is given to present the validity of theoretical derivations and the performance of the proposed observer.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1103-1108
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• 2007

This paper describes a robust model-based roll state estimator for application to the detection of impending vehicle rollover. The roll state estimator is based on a 2-D bicycle model and a roll model to estimate the maneuver-induced vehicle roll motion. The measurement signals are lateral acceleration, yaw rate, steering angle, and vehicle speed. Vehicle mass is adapted to obtain robust performance of the estimator. Computer simulation is conducted to evaluate the proposed roll state estimator by using a validated vehicle simulator. It is shown that the roll state estimator shows robust performance without exact vehicle mass information.

• Journal of Power Electronics
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• v.13 no.5
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• pp.814-828
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• 2013

The LCL resonant inductive power transfer (IPT) system is increasingly used because of its harmonic filtering capabilities, high efficiency at light load, and unity power factor feature. However, the modeling and controller design of this system become extremely difficult because of parameter uncertainty, high-order property, and switching nonlinear property. This paper proposes a frequency and load uncertainty modeling method for the LCL resonant IPT system. By using the linear fractional transformation method, we detach the uncertain part from the system model. A robust control structure with weighting functions is introduced, and a control method using structured singular values is used to enhance the system performance of perturbation rejection and reference tracking. Analysis of the controller performance is provided. The simulation and experimental results verify the robust control method and analysis results. The control method not only guarantees system stability but also improves performance under perturbation.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.520-526
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• 2003

In the case of a strapdown inertial navigation system (SDINS) with sizeable attitude errors, the uncertainty caused by linearization of the system degrades the performance of the filter. In this paper, a robust filter and various error models for the uncertainty are presented. The analytical characteristics of the proposed filter are also investigated. The results show that the filter does not require the statistical property of the system disturbance and that the region of the estimation error depends on a freedom parameter in the worst case. Then, the uncertainty of the SDINS is derived. Depending on the choice of the reference frame and the attitude error state, several error models are presented. Finally, various in-flight alignment methods are proposed by combining the robust filter with the error models. Simulation results demonstrate that the proposed filter effectively improves the performance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.22-30
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• 2003

A Coordinate-Transformation Extended Robust Kalman Filter (CERKF) designed in the Krein space is proposed, and then applied to a nonlinear incoming ballistic missile tracking system with parameter uncertainties. First, the Extended Robust Kalman filter (ERKF) is proposed to handle the nonlinearity of measurement equation which occurs whenever the polar coordinate system is transformed into the Cartesian coordinate system. Moreover, linearization error inevitably occurs and deteriorates the tracking performance, which is considerably reduced by the proposed CERKF. Through the simulation results, we show that the proposed CERKF, which uses the measurement coordinate system, has less RMS error than the previous ERKF which is designed in the Krein space using the Cartesian system. We also verify that the robustness and the stability of the proposed filter are guaranteed in two radars: the phased way radar and the scanning radar

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.967-972
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• 2001

In this paper, an adaptive robust nonlinear predictive controller is developed for the continuous time nonlinear systems whose control objective is composed of the system output and its desired value. The basic control law is derived from the continuous time prediction model and its feedback dynamcis shows another from if input and output linearization. In order to cope with the parameter uncertainty, robust control is incorporated into the basic control law and the asymptotic convergence of tracking error to a certain bounded region is guaranteed. For stability and performance improvement within the bounded region, an adaptive control is introduced. Simulation tests for the motion control of an underwater wall-ranging robot confirm the performance improvement and the robustness of this controller.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.62-74
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• 2002

The tracking of a reference temperature trajectory in a polymerization batch reactor is a common problem and has critical importance because the quality control of a batch reactor is usually achieved by implementing the trajectory precisely. In this study, only energy balances around a reactor are considered as a design model for control synthesis, and material balances describing concentration variations of involved components are treated as unknown disturbances, of which the effects appear as time-varying parameters in the design model. For the synthesis of a tracking controller, a method combining the input-output linearization of a time-variant system with the parameter estimation is proposed. The parameter estimation method provides parameter estimates such that the estimated outputs asymptotically follow the measured outputs in a specified way. Since other unknown external disturbances or uncertainties can be lumped into existing parameters or considered as another separate parameters, the method is useful in practices exposed to diverse uncertainties and disturbances, and the designed controller becomes robust. And the design procedure and setting of tuning parameters are simple and clear due to the resulted linear design equations. The performances and the effectiveness of the proposed method are demonstrated via simulation studies.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.109-118
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• 2005

The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.