Structure of multivariable fuzzy control systems with a coordinator
Fuzzy Sets and Systems - Special issue on fuzzy control
Fuzzy-neural control: principles, algorithms and applications
Fuzzy-neural control: principles, algorithms and applications
System-on-programmable-chip implementation for on-line face recognition
Pattern Recognition Letters
Application of adaptive fuzzy control to ac machines
Applied Soft Computing
Decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for a Lorenz chaotic problem
Expert Systems with Applications: An International Journal
A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour
Expert Systems with Applications: An International Journal
SOGARG: A self-organized genetic algorithm-based rule generation scheme for fuzzy controllers
IEEE Transactions on Evolutionary Computation
Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Paper: A linguistic self-organizing process controller
Automatica (Journal of IFAC)
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As far as fuzzy logic based multivariable control systems are concerned, it is not always an easy task to express control strategies in the form of related multi-situations to multi-actions control rules. Decoupled control is one possible and attractive strategy to simplify this problem. However, the control performance of the decoupled controller relies greatly on 'a prior' knowledge of the system dynamics to build suitable compensators. This paper aims at introducing a new model-independent decoupled control architecture with the ability of on-line learning, which ensures a fast tracking performance. In this architecture, the dominating controller is developed using a new model-free Self-Organizing Fuzzy Logic Control (SOFLC) architecture whereby the Performance Index table is 'dynamic', of a free structure, and starting from no knowledge. Furthermore, a switching mode scheme, with a compensating action triggered by the interaction between the channels, is proposed to improve the tracking performance of the closed-loop system. A series of simulations are carried out on a two-input and two-output biomedical process, with the conclusion that the proposed control mechanism has the ability to deal with varying system dynamics and noise and is tolerant to the choice of the compensator gains effectively.