An introduction to fuzzy control
An introduction to fuzzy control
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Stability analysis of fuzzy control systems
Fuzzy Sets and Systems
Robust Control Design with MATLAB® (Advanced Textbooks in Control and Signal Processing)
Robust Control Design with MATLAB® (Advanced Textbooks in Control and Signal Processing)
Design and stability analysis of single-input fuzzy logiccontroller
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Technical Communique: H∞ stability robustness of fuzzy control systems
Automatica (Journal of IFAC)
Brief Robust stability of fuzzy control systems based on conicity conditions
Automatica (Journal of IFAC)
Robustness of systems with various PI-like fuzzy controllers for industrial plants with time delay
FS'08 Proceedings of the 9th WSEAS International Conference on Fuzzy Systems
Design method for robust PI-like process fuzzy logic controller
FS'08 Proceedings of the 9th WSEAS International Conference on Fuzzy Systems
Design and implementation of real-time fuzzy control for thermodynamic plant
FS'08 Proceedings of the 9th WSEAS International Conference on Fuzzy Systems
Robustness of systems with various PI-like fuzzy controllers for industrial plants with time delay
WSEAS Transactions on Circuits and Systems
Design of robust fuzzy logic controllers for complex non-linear processes with time delay
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Robust fuzzy parallel distributed compensation PI control of non-linear plant
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
International Journal of Advanced Intelligence Paradigms
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Most industrial plants are complex, nonlinear, time-varying with time delay, difficult to control. The single input fuzzy controllers (SI FCs) are promising in satisfying the high performance demands to the control systems of such plants and ensuring the fuzzy system stability, coping with system nonlinearity, plant model uncertainty and SI FC design. The aim of the present investigation is to derive robust stability requirements for an incremental PI-like SI FC closed loop system that serve the development of a SI FC design method for the control of industrial plants with time delay and model uncertainty. The main contributions are: 1) modification of the Popov stability approach to study the SI FC system stability and to derive design rules; 2) derivation of robust stability criterion for a SI FC system and on its basis development of a SI FC tuning algorithm. The results are applied for the stabilisation of the air temperature in a furnace using MATLAB™. The advantages of the designed SI FC system are assessed in comparison with ordinary PI control system via simulations. The SI FC system preserves stability for a great range of plant uncertainties. The tradeoff is longer settling time of the nominal system transient responses.