Chun-Hsiung Fang

Bio: Chun-Hsiung Fang is an academic researcher from National Kaohsiung University of Applied Sciences. The author has contributed to research in topics: Robust control & Fuzzy control system. The author has an hindex of 14, co-authored 73 publications receiving 1463 citations. Previous affiliations of Chun-Hsiung Fang include National Sun Yat-sen University.

A simple method to calculate the exact bound for robust stability of uncertain discrete time generalized state-space systems

Abstract: By transforming the robustness problem to a rank problem, an analytic method is proposed to compute the exact bound for robust stability of discrete-time generalized state-space systems with unidirectional perturbations. Application of the proposed method to solving the pole-clustering robustness inside a specified disk for both continuous-time and discrete-time uncertain generalized state-space systems is also presented.

Self-constructing recurrent fuzzy neural network for ultrasonic motor drive

Abstract: The ultrasonic motor (USM) is a popular actuator and used in industry and academic research in the last decades because it has the advantages of high holding torque, good response characteristics, high torque density, silent operation, free of electromagnetic noise and compact size However, the motor are time-varying and highly nonlinear system which the parameters varied with increasing temperature and changes in drive frequency, load torque and phase difference of two-phase voltages The investigations are focused on three parts, first we construct complex dynamic model of traveling-wave ultrasonic motor (TWUSM) by MATLAB/ SIMULINK in this paper, a hybrid model which combines the strength of the equivalent circuit method and the finite element method is derived, On the other hand, a novel controller and driving system are presented For the part of control, we present a self-constructing recurrent fuzzy neural network for the speed control of a TWUSM to track periodic reference trajectories Two types of online learning algorithms are the structure learning and the parameter learning The structure learning has the ability of identifying whether the fuzzy rules are generated or not, while the parameter learning algorithm used the supervised gradient decent method to adjust the connected weights in the consequent part When the system is in steady state, fuzzy decision-making method is used to delete unimportant fuzzy rules automatically, so that to get the simplest structure of SCRFNN while maintaining the good control performance Finally, simulation results show that the control effort is effective, as well as confirm the theoretical work

Exact bounds for robust stability of generalized state-space systems with perturbations on derivative state matrix

Abstract: The stability robustness of uncertain generalized state-space systems with unidirectional perturbations on derivative state matrix is investigated. The exact bound can be easily obtained by only checking the stability of some finite real points.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

Robust stability and stabilization for singular systems with state delay and parameter uncertainty

Abstract: Considers the problems of robust stability and stabilization for uncertain continuous singular systems with state delay. The parametric uncertainty is assumed to be norm bounded. The purpose of the robust stability problem is to give conditions such that the uncertain singular system is regular, impulse free, and stable for all admissible uncertainties, while the purpose of the robust stabilization is to design a state feedback control law such that the resulting closed-loop system is robustly stable. These problems are solved via the notions of generalized quadratic stability and generalized quadratic stabilization, respectively. Necessary and sufficient conditions for generalized quadratic stability and generalized quadratic stabilization are derived. A strict linear matrix inequality (LMI) design approach is developed. An explicit expression for the desired robust state feedback control law is also given. Finally, a numerical example is provided to demonstrate the application of the proposed method.

Stability Analysis of Interval Type-2 Fuzzy-Model-Based Control Systems

Abstract: This paper presents the stability analysis of interval type-2 fuzzy-model-based (FMB) control systems. To investigate the system stability, an interval type-2 Takagi-Sugeno (T-S) fuzzy model, which can be regarded as a collection of a number of type-1 T-S fuzzy models, is proposed to represent the nonlinear plant subject to parameter uncertainties. With the lower and upper membership functions, the parameter uncertainties can be effectively captured. Based on the interval type-2 T-S fuzzy model, an interval type-2 fuzzy controller is proposed to close the feedback loop. To facilitate the stability analysis, the information of the footprint of uncertainty is used to develop some membership function conditions, which allow the introduction of slack matrices to handle the parameter uncertainties in the stability analysis. Stability conditions in terms of linear matrix inequalities are derived using a Lyapunov-based approach. Simulation examples are given to illustrate the effectiveness of the proposed interval type-2 FMB control approach.

Fuzzy-Model-Based Reliable Static Output Feedback $\mathscr{H}_{\infty }$ Control of Nonlinear Hyperbolic PDE Systems

Abstract: This paper investigates the problem of output feedback robust $\mathscr{H}_{\infty }$ control for a class of nonlinear spatially distributed systems described by first-order hyperbolic partial differential equations (PDEs) with Markovian jumping actuator faults. The nonlinear hyperbolic PDE systems are first expressed by Takagi–Sugeno fuzzy models with parameter uncertainties, and then, the objective is to design a reliable distributed fuzzy static output feedback controller guaranteeing the stochastic exponential stability of the resulting closed-loop system with certain $\mathscr{H}_{\infty }$ disturbance attenuation performance. Based on a Markovian Lyapunov functional combined with some matrix inequality convexification techniques, two approaches are developed for reliable fuzzy static output feedback controller design of the underlying fuzzy PDE systems. It is shown that the controller gains can be obtained by solving a set of finite linear matrix inequalities based on the finite-difference method in space. Finally, two examples are presented to demonstrate the effectiveness of the proposed methods.