Wei-Ling Chiang

T-S fuzzy controllers for nonlinear interconnected systems with multiple time delays

This paper investigates the effectiveness of a passive tuned mass damper (TMD) and fuzzy controller in reducing the structural responses subject to the external force. In general, TMD is good for linear systems. We proposed here an approach of Takagi-Sugeno (T-S) fuzzy controller to deal with the nonlinear system. To overcome the effect of modeling error between nonlinear multiple time-delay systems and T-S fuzzy models, a robustness design of fuzzy control via model-based approach is proposed in this paper. A stability criterion in terms of Lyapunovs direct method is derived to guarantee the stability of nonlinear multiple time-delay interconnected systems. Based on the decentralized control scheme and this criterion, a set of model-based fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time-delay interconnected system and the H/sup /spl infin// control performance is achieved at the same time. Finally, the proposed methodology is illustrated by an example of a nonlinear TMD system.

Stability analysis of T-S fuzzy models for nonlinear multiple time-delay interconnected systems

In this paper, the Takagi-Sugeno (T-S) fuzzy model representation is extended to the stability analysis for nonlinear interconnected systems with multiple time-delays using linear matrix inequality (LMI) theory. In terms of Lyapunovs direct method for multiple time-delay fuzzy interconnected systems, a novel LMI-based stability criterion which can be solved numerically is proposed. Then, the common P matrix of the criterion is obtained by LMI optimization algorithms to guarantee the asymptotic stability of nonlinear interconnect systems with multiple time-delay. Finally, the proposed stability conditions are demonstrated with simulations throughout this paper.

Fuzzy controllers for nonlinear interconnected tmd systems with external force

Abstract This paper investigates the effectiveness of a passive tuned mass damper (TMD) and active fuzzy controllers in reducing structural responses under external forces. In general, TMD is good for linear systems. We propose here a fuzzy controller to deal with nonlinear systems. For the fuzzy controller, a stability criterion in terms of Lyapunovs direct method is derived to guarantee the stability of interconnected TMD systems. Based on decentralized control and this criterion, a set of model‐based fuzzy controllers are then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear interconnected TMD system. Finally, an example is given to illustrate the concepts discussed throughout this paper.

APPLICATION AND ROBUSTNESS DESIGN OF FUZZY CONTROLLER FOR RESONANT AND CHAOTIC SYSTEMS WITH EXTERNAL DISTURBANCE

A robustness design of fuzzy control via model-based approach is proposed in this paper to overcome the effect of approximation error between nonlinear system and Takagi-Sugeno (T-S) fuzzy model. T-S fuzz model is used to model the resonant and chaotic systems and the parallel distributed compensation (PDC) is employed to determine structures of fuzzy controllers. Linear matrix inequality (LMI) based design problems are utilized to find common definite matrices P and feedback gains K satisfying stability conditions derived in terms of Lyapunov direct method. Finally, the effectiveness and the feasibility of the proposed controller design method is demonstrated through numerical simulations on the chaotic and resonant systems.

Application of geographic information system to the allocation of disaster shelters via fuzzy models

Purpose – Fuzzy theory provides a rigorous, flexible approach to the problem of defining and computing. Therefore, to facilitate decision making in a geographic information system (GIS), the graph layer indicator and the Takagi‐Sugeno (T‐S) fuzzy model must be integrated. This study aims to explain several versions of the T‐S fuzzy model based on fuzzy theory and fuzzy operation.Design/methodology/approach – An inference model is constructed for GIS using the T‐S fuzzy model to formulate an integrated T‐S decision‐making (TSDMK) system.Findings – The TSDMK system accommodates inexact, linguistic, vague and uncertain GIS data. The operator assigns most graph layer indicators by intuition.Practical implications – Simulation results for the Hualien main station show that the proposed TSDMK system is an effective approach for GIS decision making.Originality/value – This investigation assesses applications of fuzzy logic for decision making in a GIS based on TSDMK graphs focusing on model‐based systems.

Fuzzy Control for Nonlinear Systems via Neural-Network-Based Approach

The stabilization problem is considered in this study for a nonlinear system. It is shown that the stability analysis of nonlinear systems can be reduced into linear matrix inequality (LMI) problems. First, the neural-network (NN) model is employed to approximate a nonlinear system via the backpropagation algorithm. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. In terms of Lyapunovs direct method, a sufficient condition is provided to guarantee the stability of nonlinear systems. Based on this criterion, a model-based fuzzy controller is then designed to stabilize the nonlinear system and the H∞ control performance is achieved at the same time. Finally, two examples with numerical simulations are given to illustrate the control methodology.

STABILITY ANALYSIS OF NONLINEAR INTERCONNECTED SYSTEMS VIA T–S FUZZY MODELS

This paper is concerned with the stability problem of nonlinear interconnected systems. Each of them consists of a few interconnected subsystems which are approximated by Takagi–Sugeno (T–S) type fuzzy models. In terms of Lyapunovs direct method, a stability criterion is derived to guarantee the asymptotic stability of interconnected systems. It is shown that the stability analysis problems of nonlinear interconnected systems can be reduced to linear matrix inequality (LMI) problems via suitable Lyapunov functions and T–S fuzzy techniques. Finally, numerical examples with simulations are given to demonstrate the validity of the proposed approach.

Application of Fuzzy H∞ Control via T–S Fuzzy Models for Nonlinear Time-Delay Systems

This paper deals with the problem of stability analysis and stabilization via Takagi-Sugeno (T-S) fuzzy models for nonlinear time-delay systems. First, Takagi-Sugeno (T-S) fuzzy models and some stability results are recalled. To design fuzzy controllers, nonlinear time-delay systems are represented by Takagi-Sugeno fuzzy models. The concept of parallel-distributed compensation (PDC) is employed to determine structures of fuzzy controllers from the T-S fuzzy models. LMI-based design problems are defined and employed to find feedback gains of fuzzy controller and common positive definite matrices P satisfying stability a delay-dependent stability criterion derived in terms of Lyapunov direct method. Based on the control scheme and this criterion, a fuzzy controller is then designed via the technique of PDC to stabilize the nonlinear time-delay system and the H∞ control performance is achieved in the mean time. Finally, the proposed controller design method is demonstrated through numerical simulations on the chaotic and resonant systems.

H/sup /spl infin// fuzzy control of structural systems using Takagi-Sugeno fuzzy model

This paper proposed a design method of H/sup /spl infin// control performance for structural systems using Takagi Sugeno (T-S) fuzzy model. The structural system with tuned mass damper was modeled by T-S type fuzzy model. By using parallel distributed compensation (PDC) scheme, we design a nonlinear fuzzy controller for the tuned mass damper system. This control problem could be reformulated into linear matrix inequalities (LMI) problem. Furthermore, the tuned mass damper could be designed according to the first mode of frequency of the control system and then the fuzzy controller could be found via Matlab LMI toolbox to stabilize the structural system. A simulation example was given to show the feasibility of the proposed fuzzy controller design method.