Fuzzy methods control

While the single-loop PID controller is satisfactoiy in many process apphcations, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods hereafter that can be implemented via computer control, namely feedforward control, cascade control, time-delay compensation, selective and override control, adaptive control, fuzzy logic control, and statistical process control. 

Defuzzification is the procedure for mapping from a set of inferred fuzzy control signals contained within a fuzzy output window to a non-fuzzy (crisp) control signal. The centre of area method is the most well known defuzzification technique, which in linguistic terms can be expressed as... 

A rule based approach to process control has for many years provided an alternative to traditional methods in the form of fuzzy logic control (8,9). Since the advent of expert systems, rulebases have been used for fault diagnosis [10], to advise operators (11) 9 to aid control engineers when installing PID controllers (12), to provide expert on-line tuning for PID controllers (13), and to control processes without the use of fuzzy logic (14,15). 

Dazzi, D., Taddei, F., Gavaiini, A., Uggeri, E., Negro, R. and Pezzarossa, A. (2001) The control of blood glucose in the critical diabetic patient a neuro-fuzzy method. Journal of Diabetes and its Complications, 15 (2), 80-7. 

Artificial Intelligence in Chemistry Chemometrics Multivariate View on Chemical Problems Fuzzy Methods in Chemistry Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry Neural Networks in Chemistry Quality Control, Data Analysis Structural Similarity Measures for Database Searching Structure and Substructure Searching Structure Determination by Computer-based Spectrum Interpretation Structure Generators Structure Representation. 

Traditional control systems are in general based on mathematical models that describe the control system using one or more differential equations that define the system response to its inputs. In many cases, the mathematical model of the control process may not exist or may be too expensive in terms of computer processing power and memory. In these cases a system based on empirical rules may be more effective. In many cases, fuzzy control can be used to improve existing controller systems by adding an extra layer of intelligence to the current control method. 

Bandemer considered the role of fuzzy set theory in analytical chemistry. The applications they described focused on pattern recognition problems, the calibration of analytical methods,quality control, and component identification and mixture evaluation. Gordon and Somorjai applied a fuzzy clustering technique to the detection of similarities among protein substructures. A molecular dynamics trajectory of a protein fragment was analyzed. In the following subsections, some applications based on the hierarchical fuzzy clustering techniques presented in this chapter are reviewed. 

The problems encountered in mathematical modeling of tumble/growth agglomeration do not relate to the theories, formulas, and possibilities to solve the ever more complicated equations. With modem computing possibilities, a whole series of assumptions can be introduced into the model equations and responses to certain imaginary process conditions can be predicted. However, the real system often produces unexpected results intermittently or even consistently without offering a clear indication of why such deviations occur. Introduction of new mathematical methods, such as, for example, fuzzy logic or chaos theory, produce more complicated model equations and closer to life results but still are not able to serve as unequivocal bases for control schemes. 

Barto, A.G. 1992. Reinforcement learning and adaptive critic methods. In Handbook of Intelligent Control Neural, Fuzzy and Adaptive Approaches. D.A. White and D.A. Sofge, Eds. pp. 469—492. Van Nostrand Reinhold, New York. 

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