Fuzzy logic control systems

Fuzzy logic control systems 10.2.1 Fuzzy set theory... 

Fig. 10.17 Self-Organizing Fuzzy Logic Control system.

In addition to model-based control schemes using population balance equations, there are a number of practical control schemes in the pharmaceutical industry, that do not rely on mathematical models. These include simple feedback control with or without feed-forward compensation, and fuzzy-logic control systems. 

A fuzzy perception is an assessment of a physical condition that is not measured with precision, but is assigned an inmitive value. No matter how good the measuring equipment is, everything in the universe has fuzziness to a certain extent. The fuzzy perceptions can serve as a basis for processing and analysis in a fuzzy logic control system. 

Human processing of information is not based on two-valued, off-on, either-or logic. It is based on fuzzy perceptions, fuzzy truths, fuzzy inferences, etc., all resulting in an averaged, summarized, normalized output, which is given by the human a precise number or decision value which he/she verbalizes, writes down or acts on. It is the goal of fuzzy logic control systems to also do this. 

Sometimes fuzzy logic controllers are combined with pattern recognition software such as artificial neural networks (Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, New Jersey, 1992). 

MATLAB Fuzzy Inference System (FIS) editor can be found in Appendix 1. Figure 10.16 shows the control surface for the 11 set rulebase fuzzy logic controller. 

Self-Organizing Fuzzy Logic Control (SOFLC) is an optimization strategy to create and modify the control rulebase for a FLC as a result of observed system performance. The SOFLC is particularly useful when the plant is subject to time-varying parameter changes and unknown disturbances. 

The angular positional control system shown by the block diagram in Figure 10.36 is to have the velocity feedback loop removed and controller K replaced by a fuzzy logic controller (FLC) as demonstrated by Barrett (1992). The inputs to the FLC... 

It may be useful to point out a few topics that go beyond a first course in control. With certain processes, we cannot take data continuously, but rather in certain selected slow intervals (c.f. titration in freshmen chemistry). These are called sampled-data systems. With computers, the analysis evolves into a new area of its own—discrete-time or digital control systems. Here, differential equations and Laplace transform do not work anymore. The mathematical techniques to handle discrete-time systems are difference equations and z-transform. Furthermore, there are multivariable and state space control, which we will encounter a brief introduction. Beyond the introductory level are optimal control, nonlinear control, adaptive control, stochastic control, and fuzzy logic control. Do not lose the perspective that control is an immense field. Classical control appears insignificant, but we have to start some where and onward we crawl. 

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). 

Lin, C.T. and Lee, C.S.G., Neural-network-based fuzzy logic control and decision system, IEEE Trans. Comput. C-40 1320-1336,1991. 

Three approaches dominate the real-time intelligent control field (1) expert systems, (2) neural net controllers (neurocontrollers), and (3) fuzzy logic controllers [50]. These intelligent control systems are based on two types of information processing symbolic and subsymbolic processing [15,51]. 

Homayouni SM, Hong TS, Ismail N (2(X)9) Development of genetic fuzzy logic controllers for complex production systems. Comput Ind Eng 57 1247-1257... 

Tong C.M., 2002. Evolutionary Design of Fuzzy-logic Controllers with Minimal Rule Sets for Manufacturing Systems. Ph.D Thesis, The University of Hong Kong. 

ANN has also been applied to flow control in microfluidic networks. Assadsangabi et al. [13] presented a combined feedback/feedforward strategy to control the output flow rate in the T-juncti(Mi of microchannels. A finite element model (FEM) was used to generate the training data, and a combined ANN and fuzzy logic (FL) system was utilized to build an inverse model of the flow in the T-junction, which serves as a controller to adjust the output flow rate. 

To reach the control objective, we are based on the procedure of [14], we are deriving a r(t) of a specific vjt) that controls the steering system (5.1) using a Fuzzy Logic Controller (FLC). A general structure of tracking control system is presented in the Fig. 5.3. 

S5mians, M. D., Kelly, S. W. (1999). Fuzzy logic control of bridge structures using intelligent semi-active seismic isolation systems. Earthquake Engineering Structural Dynamics, 25(1), 37-60. doi 10.1002/(SICI) 1096-9845(199901)28 K37 AID-EQE803>3.0.CO 2-Z... 

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