An architecture for adaptive fuzzy control in industrial environments

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.

[1]  A. Alique,et al.  Embedded fuzzy-control system for machining processes: Results of a case study , 2003, Comput. Ind..

[2]  Sung Kyung Hong,et al.  Stable fuzzy control system design with pole-placement constraint: an LMI approach , 2003, Comput. Ind..

[3]  Tsung-Chih Lin,et al.  Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems , 2002, IEEE Trans. Fuzzy Syst..

[4]  Elias Todorovich,et al.  Genetic algorithms and fuzzy control: a practical synergism for industrial applications , 2003, Comput. Ind..

[5]  Saeed Gazor,et al.  Hybrid adaptive fuzzy identification and control of nonlinear systems , 2002, IEEE Trans. Fuzzy Syst..

[6]  Tsung-Chih Lin,et al.  Combined Direct/indirect Adaptive Fuzzy-neural Control With State Observer & Supervisory Controller for Unknown Nonlinear Dynamic Systems. , 2001 .

[7]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[8]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[9]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control , 1994 .

[10]  Stefan Preitl,et al.  PI predictive fuzzy controllers for electrical drive speed control: methods and software for stable development , 2003, Comput. Ind..

[11]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[12]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[13]  Iván Rodríguez,et al.  Fuzzy control of a multiple hearth furnace , 2004, Comput. Ind..

[14]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[15]  Li-Xin Wang,et al.  Stable adaptive fuzzy controllers with application to inverted pendulum tracking , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[16]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[17]  M. MendelJ.,et al.  Fuzzy basis functions , 1995 .

[18]  Li-Xin Wang,et al.  Stable adaptive fuzzy control of nonlinear systems , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[19]  C. M. Lim.  Implementation and experimental study of a fuzzy logic controller for DC motors , 1995 .

[20]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .