Fuzzy control

Fuzzy control, which is based on fuzzy sets theory proposed by Zadeh [9], can easily utilize empirical knowledge gained from skilled operators by employing 

---

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... 

Hence, for given error of 2.5, and a rate of change of error of —0.2, the control signal from the fuzzy controller is 3.83. 

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. 

Gegov, A., Distributed Fuzzy Control of Multivariable Systems, Kluwer, 1996. 

In order to understand the application of FUZZY CONTROL in the development of washing processes, a short explanation of conventional washing techniques will be necessary. 

At the end of an additional time T2 the total quantity of absorbed water is measured. This term Suction Amount is defined as the total absorbing capacity of the introduced load. Through this, a large amount of absorbed water corresponds to a large load, a small amount of water to a small load. The results of the experiments show that the type and size of a washing load is related to the Suction Speed and the Suction Amount . Apart from this recognized tendency (Fig. 5.67), the results are somewhat fuzzy and thus justify the use of FUZZY CONTROL . 

Menon, S., and Y. Sun. 1996. Fuzzy control of reheat buzz. AIAA Paper No.96-2759. 

Nakamura, T., Kuratani, T., and Morita, Y. (1985) Fuzzy control application to glutamic acid fermentation. Proceedings of IFAC Modelling and Control of Biotechnology Processes, pp. 211215. 

FLC system approach can be used to solve problems. Many applications of FLC are related to simple control algorithms such as the PID controller. In a natural way, nonlinearities and exceptions are included which are difficult to realize when using conventional controllers. In conventional control, many additional measures have to be included for the proper functioning of the controller anti-resist windup, proportional action, retarded integral action, etc. These enhancements of the simple PID controller are based on long-lasting experience and the interface of continuous control and discrete control. The fuzzy PID-like controller provides a natural way to applied controls. The fuzzy controller is described as a nonlinear mapping. 

In practice, SMB processes are controlled using similar manual schemes (Kiisters et al., 1995, Juza, 1999 and Miller et al., 2003). Antia (2003) suggested that these heuristic rules are included in a fuzzy controller to achieve full automatic control of SM B processes, but no applications have been described so far. Cox et al. (2003) recently reported a successful control and monitoring system for the separation of an enantiomer mixture based on the concentration profiles in the recycle loop. 

Watano S, Numa T, Miyanami K, Osako Y. A fuzzy control system of high shear granulation using image processing. Powder Technol 2001 115 124-130. 

Fuzzy inference systems are also known as fuzzy associative memories, fuzzy models, fuzzy-rule-based systems, or fuzzy controllers. 

Fuzzy logic for fuzzy control and for approximate reasoning. 

Fuzzy control systems are ones in which experts decision-making rnles are used to produce a control output. The rules may either be mathematically based or not, and the controller output is usually sufficiently correct to perform the intended function. However, control with a Fuzzy system is not usually very precise. 

Develop a fuzzy control algorithm to go from your home to a favorite store. 

Davoodi, R. and Andrews, B.f., Computer simulation of FES standing up in paraplegia a self-adaptive fuzzy controller with reinforcement learning, IEEE Trans. Rehabil. Eng. TRE-6 151-161, 1998. 

Sugeno, M., Industrial Applications of Fuzzy Control. Elsevier Science, New York, 1985. 

Hellendoorn, H. and Thomas, C., Defuzzification in fuzzy controllers, J. Intel. Fuzzy Syst. 1 109-123,1993. 

---