Hwa Chang Sung

Robust digital control of fuzzy systems with parametric uncertainties: LMI-based digital redesign approach

This paper presents a new intelligent digital redesign (IDR) method for uncertain nonlinear systems which are represented by a Takagi-Sugeno (T-S) fuzzy model. The term IDR involves converting an analog controller into an equivalent digital one in the sense of state-matching. The IDR problem can be reduced to finding the digital fuzzy gains minimizing the norm distance between the closed-loop states of the analog and digital control systems. The uncertainties in the plant dynamics are considered in the IDR condition that plays an important role in the performance improvement. Also, the robust stability is well guaranteed in the proposed IDR procedure. Its constructive conditions are expressed as linear matrix inequalities (LMIs). Two numerical examples, the chaotic Lorenz system and the Qi system, are demonstrated to visualize the feasibility of the proposed methodology.

Observer-based sampled-data control for uncertain nonlinear systems: intelligent digital redesign approach

In this paper, we concern an intelligent digital redesign(IDR) method for a Takagi-Sugeno (T-S) fuzzy observer-based output-feedback control (FOBOFC) system which includes parametric uncertainties. The term IDR is to convert an existing analog control into an equivalent digital counterpart via state-matching. The considered IDR problem is viewed as convex minimization problem of the norm distances between linear operators to be matched and its constructive condition is formulated in terms of linear matrix inequalities (LMIs). The main features of the proposed method are that the state estimation error in the plant dynamics is considered in the IDR condition that plays a crucial role in the performance improvement; the uncertainties in the plant dynamics is shown in the IDR condition by virtue of the bilinear and inversebilinear approximation method; finally, the stability property is preserved by the proposed IDR method.

Robust Fuzzy Controller for Mitigating the Fluctuation of Wind Power Generator in Wind Farm

This paper proposes the implementation of robust fuzzy controller for designing intelligent wind farm and mitiagating the fluctuation of wind power generator. The existing researches are limited to individual wind turbine with variable speed so that it is necessary to study the multi-agent wind turbine power system. The scopes of these studies include from the arrangements of each power turbine to the control algorithms for the wind farm. For solving these problems, we introduce the composition of intelligent wind farm and use the T-S (Takagi-Sugeno) fuzzy model which is suitable for designing fuzzy controller. The control object in wind farm enables the minimizing the fluctuation of wind power generator. Simulation results for wind fram which is modelled as mathematically are demonstrated to visualize the feasibility of the proposed method.

Fuzzy Modeling and Robust Stability Analysis of Wind Farm based on Prediction Model for Wind Speed

This paper proposes the fuzzy modeling and robust stability analysis of wind farm based on prediction model for wind speed. Owing to the sensitivity of wind speed, it is necessary to study the dynamic equation of the variable speed wind turbine. In this paper, based on the least-square method, the wind speed prediction model which is varied by the surrounding environment is proposed so that it is possible to evaluate the practicability of our model. And, we propose the composition of intelligent wind farm and use the fuzzy model which is suitable for the design of fuzzy controller. Finally, simulation results for wind farm which is modeled mathematically are demonstrated to visualize the feasibility of the proposed method.

Robust Stability Analysis of Hybrid Magnetic Bearing System

This paper propose the robust stability algorithm for controlling a hybrid magnetic bearing system. The control object in the magnetic bearing system enables the rotor to rotate without any physical contact by using magnetic force. Generally, the system dynamics of the magnetic bearing system has severe nonlinearity and uncertainty so that it is not easy to obtain the control objective. For solving these problems, we propose the fuzzy modelling and robust control algorithm for hybrind magnetic bearing system. The sufficient conditions for robust controller are obtained in terms of solutions to linear matrix inequalities (LMIs). Simulation results for HMB are demonstrated to visualize the feasibility of the proposed method.

Robust Observer-based Fuzzy Control for Variable Speed Wind Power System

Abstract Abstract In this paper, the observer-based control for stabilizing the variable speed wind power (VSWP) system is proposed. For achieving the robust stability, we deal with the parametric uncertainties of the concerned system which is based on the Takagi-Sugeno (T–S) fuzzy model. Also, we derive the observer-based models preserving the property and structure of the uncertainties. The sufficient conditions for output feedback stabilizing controller designs are given in terms of solutions to a set of linear matrix inequalities (LMIs). The simulation results for VSWP system are demonstrated to visualize the feasibility of the proposed method.

Output-feedback sampled-data control for uncertain nonlinear system

In this paper, we concern an intelligent digital re-design(IDR) method for a fuzzy observer-based output-feedback control system which includes parametric uncertainties. The term IDR is to convert an existing analog control into an equivalent digital counterpart via state-matching. The considered IDR problem is viewed as convex minimization problem of the norm distances between linear operators to be matched and its constructive condition is formulated in terms of linear matrix inequalities (LMIs). The main features of the proposed method are that the state estimation error in the plant dynamics is considered in the IDR condition that plays a crucial role in the performance improvement; the uncertainties in the plant dynamics is shown in the IDR condition by virtue of the bilinear and inverse-bilinear approximation method; finally, the stability property is preserved by the proposed IDR method.

Stability analysis for fuzzy pulse-width-modulated systems

This paper proposes the stability analysis for the fuzzy pulse-width-modulation (PWM) system which based on intelligent digital redesign (IDR) method. The term IDR is to convert an existing analog control into an equivalent digital one in the sense of state-matching. The IDR problem can be reduced to find the digital gains minimizing the norm distance between the closed-loop states of the analog and digital control. The redesigned digital controller is again converted into PWM controller for stabilizing the fuzzy PWM system.

Intelligent Diagnosis System Based on Fuzzy Classifier

In this paper, we present the development of an intelligent diagnosis system for detecting faults of the low voltage wires. The wire detecting system based on the Time-Frequency Domain Reflectometry (TFDR) algorithm shows the condition of the wires. We analyze the reflected signal which is sent from the wire detecting system and classify the fault type of the wires by using the intelligent diagnosis system. Through the TFDR, generally, the conditions of the wires are classified into the three types - damage, open and short. In order to classify the fault type efficiently, we use the fuzzy classifier which is represented as IF-THEN rules. Finally, we show the utility of the proposed algorithm by performing the simulation which is based on the data of the coaxial cable.