Shun-Hung Tsai

Prof. Dr. Shun-Hung Tsai

National Taipei University of Technology, Taipei 10608, Taiwan
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Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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27 February 2018 in IEEE Transactions on Fuzzy Systems
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Journal Article
Closed access
Junwei WangJunwei Wang
Junwei Wang
Shun-Hung TsaiShun-Hung Tsai
Shun-Hung Tsai
HLHan-Xiong Li
Han-Xiong Li
Hak Keung LamHak Keung Lam
Hak Keung Lam
This paper employs a Takagi-Sugeno (T-S) fuzzy partial differential equation (PDE) model to solve the problem of sampled-data exponential stabilization in the sense of spatial $L^\infty$ norm $\|\cdot\|_\infty$ for a class of nonlinear parabolic distributed parameter systems (DPSs), where only a few actuators and sensors are discretely distributed in space. Initially, a T-S fuzzy PDE model is assumed to be derived by the sector nonlinearity method to accurately describe the complex spatiotemporal dynamics of the nonlinear DPSs. Subsequently, a static sampled-data fuzzy local state feedback controller is constructed based on the T-S fuzzy PDE model. By constructing an appropriate Lyapunov--Krasovskii functional candidate and employing vector-valued Wirtinger's inequalities, a variation of vector-valued Poincar\'{e} inequality in 1D spatial domain, as well as a vector-valued Agmon's inequality, it is shown that the suggested sampled-data fuzzy controller exponentially stabilizes the nonlinear DPSs in the sense of $\|\cdot\|_\infty$, if the sufficient conditions presented in term of standard linear matrix inequalities (LMIs) are fulfilled. Moreover, an LMI relaxation technique is utilized to enhance the exponential stabilization ability of the suggested sampled-data fuzzy controller. Finally, the satisfactory and better performance of the suggested sampled-data fuzzy controller are demonstrated by numerical simulation results of two examples.
17 February 2020 in IEEE Transactions on Fuzzy Systems
Verified
Journal Article
Closed access
CHChi Huang
Chi Huang
XZXia Zhang
Xia Zhang
Hak Keung LamHak Keung Lam
Hak Keung Lam
Shun-Hung TsaiShun-Hung Tsai
Shun-Hung Tsai
In this paper, the synchronization of a class variable-coefficient complex networks with reaction-diffusion terms is investigated. The objective of this paper is to synthesize a state-feedback controller, which is designed based on a T-S fuzzy model of the variable-coefficient complex network with reaction-diffusion terms such that the closed-loop system is synchronized. With the support of Green formula and some matrix inequality techniques, and utilizing the upper and lower bounds of the membership functions, membership-function-dependent synchronization conditions guaranteeing the system's synchronization are obtained in the form of linear matrix inequalities (LMIs). A numerical example is presented to verify the analysis results and illustrate the effectiveness of the proposed synchronization conditions.
28 May 2018 in IEEE Transactions on Fuzzy Systems
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Journal Article
Closed access
Shun-Hung TsaiShun-Hung Tsai
Shun-Hung Tsai
CJChih-Yang Jen
Chih-Yang Jen
The H$_{\infty}$ stabilization problem for polynomial fuzzy time-delay system (PFTDS) is investigated in this paper. First, a polynomial fuzzy controller is proposed to stabilize the PFTDS. Based on Euler's homogeneity relation, homogeneous polynomial Lyapunov–Kravoskii functional (HPLKF), and the developed polynomial slack variable matrices, a novel stabilization condition without the assumption in common use is presented in terms of sum-of-squares (SOS). In order to reduce the conservative and implementation cost, the property of membership function, and the right-hand-side (RHS) technique are adopted. Also, two relaxed H$_{\infty}$ stabilization conditions for PFTDS, which may or may not share the same fuzzy rules, are proposed. To illustrate the feasibility and merit of the proposed result, three examples are provided. In the first example, a well-known Takagi–Sugeno (T–S) fuzzy time-delay system is demonstrated to show that the proposed SOS-based methods can provide a longer allowable delay time than do some existing linear matrix inequality-based methods. The second and third examples show the validity and feasibility of the proposed results for PFTDS with the same or fewer rule numbers of the polynomial fuzzy controller.
24 June 2021 in IEEE Transactions on Cybernetics
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Journal Article
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Shun-Hung TsaiShun-Hung Tsai
Shun-Hung Tsai
YCYu-Wen Chen
Yu-Wen Chen
In this article, a novel interval type-2 Takagi–Sugeno fuzzy $c$ -regression modeling method with a modified distance definition is proposed. The modified distance definition is developed to describe the distance between each data point and the local type-2 fuzzy model. To improve the robustness of the proposed identification method, a modified objective function is presented. In addition, different from most previous studies that require numerous free parameters to be determined, an interval type-2 fuzzy $c$ -regression model is developed to reduce the number of such free parameters. Furthermore, an improved ratio between the upper and lower weights is proposed based on the upper and lower membership function with each input data, and the ordinary least-squares method is adopted to establish the type-2 fuzzy model. The Box–Jenkins model and two numerical models are given to illustrate the effectiveness and robustness of the proposed results.
28 June 2016 in IEEE Access
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Journal Article
Open access
Shun-Hung TsaiShun-Hung Tsai
Shun-Hung Tsai
YCYu-Wen Chen
Yu-Wen Chen
In this paper, an identification problem for nonlinear models is explored and a novel fuzzy identification method based on the ant colony optimization algorithm is proposed. First, a modified cluster validity criterion with a fuzzy $c$ -regression model is adopted to find appropriate rule numbers of the Takagi-Sugeno fuzzy model. Then, the ant colony optimization algorithm is adopted and the sifted initial membership function and the consequent parameters of the fuzzy model are obtained. Through an improved fuzzy $c$ -regression model and the orthogonal least-squares method, the premise structure and the consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some examples are illustrated to show that the proposed method provides better approximation results and robustness than those obtained using some of the existing methods.
9 October 2019 in IEEE Transactions on Cybernetics
Verified
Journal Article
Closed access
Shun-Hung TsaiShun-Hung Tsai
Shun-Hung Tsai
Junwei WangJunwei Wang
Junwei Wang
ESEn-Shou Song
En-Shou Song
Hak Keung LamHak Keung Lam
Hak Keung Lam
This article addresses the H∞ stabilization problems for a class of nonlinear distributed parameter systems which is described by the first-order hyperbolic partial differential equations (PDEs). First, the first-order hyperbolic PDE systems are identified as a polynomial fuzzy PDE system and the polynomial fuzzy controller for the polynomial fuzzy PDE system is proposed. By utilizing the proposed homogeneous polynomial Lyapunov functional, Euler's homogeneous function theorem, and the proposed theorems, a spatial derivative sum-of-squares (SDSOS) exponential stabilization condition is proposed. In addition, a recursive algorithm for the SDSOS exponential stabilization condition is developed to find the feasible solution. Furthermore, in order to reduce the conservatism of the proposed results, a relaxed H∞ stabilization condition for the polynomial fuzzy PDE system is provided. Finally, the nonisothermal plug-flow reactor (PFR) is used to demonstrate the effectiveness and feasibility of the proposed method.