Analysis and design for a class of complex control systems part II: fuzzy controller design
Automatica (Journal of IFAC)
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Design of a stable fuzzy controller for an articulated vehicle
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Fuzzy Systems
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Advances in Engineering Software
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Advances in Engineering Software
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AEE'08 Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering
Engineering Applications of Artificial Intelligence
Modeling and control for nonlinear structural systems via a NN-based approach
Expert Systems with Applications: An International Journal
The stability of an oceanic structure with T-S fuzzy models
Mathematics and Computers in Simulation
Managing Target Cash Balance in Construction Firms Using Novel Fuzzy Regression Approach
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Fuzzy control for seismically excited bridges with sliding bearing isolation
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Stability analysis and robustness design of nonlinear systems: An NN-based approach
Applied Soft Computing
Advances in Engineering Software
A multivariate fuzzy c-means method
Applied Soft Computing
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Applied Soft Computing
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
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This paper provided the stability conditions and controller design for a class of structural and mechanical systems represented by Takagi-Sugeno (T-S) fuzzy models. In the design procedure of controller, parallel-distributed compensation (PDC) scheme was utilized to construct a global fuzzy logic controller by blending all local state feedback controllers. A stability analysis was carried out not only for the fuzzy model but also for a real mechanical system. Furthermore, this control problem can be reduced to linear matrix inequalities (LMI) problems by the Schur complements and efficient interior-point algorithms are now available in Matlab toolbox to solve this problem. A simulation example was given to show the feasibility of the proposed fuzzy controller design method.