Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Implementation and experimental study of a fuzzy logic controller for DC motors
Computers in Industry
A course in fuzzy systems and control
A course in fuzzy systems and control
Embedded fuzzy-control system for machining processes results of a case study
Computers in Industry
Stable fuzzy control system design with pole-placement constraint: an LMI approach
Computers in Industry
Genetic algorithms and fuzzy control: a practical synergism for industrial applications
Computers in Industry
Computers in Industry - Special issue: Soft computing in industrial applications
Fuzzy control of a multiple hearth furnace
Computers in Industry
Stable adaptive fuzzy controllers with application to inverted pendulum tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Automatic extraction of the fuzzy control system by a hierarchical genetic algorithm
Engineering Applications of Artificial Intelligence
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The paper presents an architecture for adaptive fuzzy control of industrial systems. Both conventional and adaptive fuzzy control can be designed. The control methodology can integrate a priori knowledge about the control and/or about the plant, with on-line control adaptation mechanisms to cope with time-varying and/or uncertain plant parameters. The paper presents the fuzzy control software architecture that can be integrated in industrial processing and communication structures. It includes four distinct modules: a mathematical fuzzy library, a graphical user interface (GUI), fuzzy controller, and industrial communication. Three types of adaptive fuzzy control methods have been studied, and compared: (1) direct adaptive, (2) indirect adaptive, and (3) combined direct/indirect adaptive. An experimental benchmark composed of two mechanically coupled electrical DC motors has been employed to study the performance of the presented control architectures. The first motor acts as an actuator, while the second motor is used to generate nonlinearities and/or time-varying load. Results indicate that all tested controllers have good performance in overcoming changes of DC motor load.