http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Wen-Jer Chang,Liang-Zhi Liu,Cheung-Chieh Ku 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.3
This paper investigates the fuzzy control problem of a class of nonlinear continuous-time stochastic systems with achieving the passivity performance. A model-based observer feedback fuzzy control utilizing the concept of so-called parallel distributed compensation (PDC) is employed to stabilize the class of nonlinear stochastic systems that are represented by the Takagi-Sugeno (T-S) fuzzy models. Based on the Lyapunov criteria, the Linear Matrix Inequality (LMI) technique is used to synthesize the observer feedback fuzzy controller design such that the closed-loop system satisfies stability and passivity constraints, simultaneously. Finally, a numerical example is given to demonstrate the applicability and effectiveness of the proposed design method.
Wen-Jer Chang,Feng-Ling Hsu,Cheung-Chieh Ku 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.4
In this paper, a stabilization problem for the discrete nonlinear system with external disturbance, multiplicativenoises and multiple constraints has been discussed in accordance with the definition of Lyapunov stability. Based on fuzzy modeling approach, the overall fuzzy model of a nonlinear plant is transformed into a class of linearsystems. Applying a Sliding Mode Fuzzy Control (SMFC) scheme, the designed controller causes the closed-loopsystem converging to the sliding surface and achieving the required control performance. For the control performance,the concepts of stability, individual state variance and passivity constraints are introduced for the slidingmode fuzzy control system. To apply convex optimal programming algorithm, some sufficient conditions derivedin this paper are reduced to Linear Matrix Inequality (LMI) problem. At last, two simulation examples are proposedto demonstrate the applicability and usefulness of the proposed design method. One of the examples is to discussthe conservatism of this paper. Another is to show that the discrete truck-trailer system controlled by sliding modefuzzy controller can achieve stability constraints, individual state variance constraints and passivity constraints.
Robust Fuzzy Control for Discrete Perturbed Time-Delay Affine Takagi-Sugeno Fuzzy Models
Wen-Jer Chang,Wei-Han Huang,Cheung-Chieh Ku 제어·로봇·시스템학회 2011 International Journal of Control, Automation, and Vol.9 No.1
The purpose of this paper is to study the stability analysis and controller synthesis principles of Discrete Perturbed Time-Delay Affine (DPTDA) Takagi–Sugeno (T–S) fuzzy models. In general, the T–S fuzzy model is a weighted sum of some linear subsystems via fuzzy membership functions. This paper considers fuzzy rules include both linear nominal parts and uncertain parameters in the time-delay affine T–S fuzzy model. For DPTDA T–S fuzzy models, the T–S fuzzy control scheme is used to confront the H∞ performance constraints. Some sufficient conditions are derived on robust H∞ disturbance attenuation in which both robust stability and a prescribed performance are required to be achieved. In order to find suitable fuzzy controllers, the Iterative Linear Matrix Inequality (ILMI) algorithm is employed to solve these sufficient conditions. At last, a numerical simulation for the nonlinear truck-trailer system is given to show the applications of the present design approach.
Wen-Jer Chang,Yan-Horng Lin,Jialu Du,Chih-Ming Chang 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.10
The stability analysis and controller design of stochastic systems have become much more important because the stochastic behaviors usually exist in practical nonlinear systems. In this paper, a robust fuzzy controller design approach is proposed with multiple constraints, including state variance constraints, output variance constraints, and pole placement constraints. At first, nonlinear systems are expressed as the Takagi-Sugeno fuzzy model, and the parallel distributed compensation method is applied to design the robust fuzzy controllers. Next, considering the stability analysis and the performance constraints of perturbed Takagi-Sugeno fuzzy models, Lyapunov conditions are developed based on covariance control theory, pole placement theory and robust control theory. By constructing the stability conditions with multiple constraints, the proposed fuzzy control problem can be effectively transferred into the linear matrix inequality problem. It can be solved by the convex optimal programming algorithm. At last, a nonlinear ship steering system is selected to verify the effectiveness and applicability of the proposed robust fuzzy controller design method.
Cheung-Chieh Ku,Wen-Jer Chang,Chun-Hung Lin,Yao-Chung Chang 제어·로봇·시스템학회 2013 International Journal of Control, Automation, and Vol.11 No.3
This paper investigates a fuzzy controller design method for discrete-time nonlinear stochastic time-delay systems which are presented by the Takagi-Sugeno (T-S) fuzzy model with multiplicative noises. Utilizing the proposed design method, the fuzzy controller can be carried out via not only state feedback scheme but also output feedback scheme. Both of them are accomplished by the concept of imperfect premise matching (IPM). For discussing the stabilization problem, the Lyapunov-Krasovskii function and passivity theory are applied to derive the sufficient conditions. Moreover, the discrete Jensen inequality is employed to decrease the conservatism of the proposed method. Finally, a numerical example for the control of a nonlinear time-delay pendulum system is provided to show the effectiveness and usefulness of the proposed design method.
Robust Decentralized Fuzzy Control for Large-scale Descriptor Systems With Decay Rate Constraint
Che-Lun Su,Wen-Jer Chang,Cheung-Chieh Ku 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.12
This study addresses the control problem of a nonlinear large-scale descriptor system (LSDS) through the use of a decentralized proportional-plus-derivative state feedback fuzzy (DPDF) control strategy. The TakagiSugeno (T-S) fuzzy modeling technique, which is widely used by researchers, is used to describe the nonlinear LSDS as a set of linear subsystems with interconnections. Based on the constructed T-S fuzzy LSDS, a DPDF feedback method is proposed to address the limitations of the descriptor matrix of the T-S fuzzy LSDS and to solve the regular and impulse-free problems of systems while providing a more convenient and effective approach for discussing system stability. Additionally, the Lyapunov theory is chosen to analyze the stability conditions. This study uses a quadratic Lyapunov function to provide sufficient criteria to ensure the decay rate performance of the LSDS, which can be transformed into linear matrix inequality (LMI) form. The proposed method is demonstrated through several examples to showcase its effectiveness.