CSEN 1004

2 lecture hours
2 exercise hours

4  ECTS credits

Fuzzy Logic Control and Hybrid Systems


  • Fuzzy Logic Systems (FLSs) is much closer in spirit to human thinking and natural language than traditional logical systems. Fuzzy Logic attempts to mimic the way of human thinking to reason in an approximate way rather than a precise way. For example, we don’t say “If the temperature is above 24 degrees and the cloud cover is less than 10% and I have 3 hours time, I will go for a hike with a probability of 0.47.” We rather say “If the weather is nice and I have a little time, I will probably go for a walk”. Fuzzy Logic can play an important role to model and represent imprecise and uncertain linguistic human concepts such as Hot, Cold, etc.

    Basically a FLS provides an effective means of capturing the approximate, inexact nature of the real world. In this respect a FLS can be viewed as an algorithm which converts the linguistic control strategy based on expert knowledge into an automatic control strategy. Experience has shown that a FLS yields results superior to those obtained by conventional control algorithms. In particular, the methodology of the FLS appears very useful when the processes are too complex for analysis by conventional quantitative techniques or when the available sources of information are interpreted qualitatively, inexactly or uncertainly. Fuzzy Logic holds great promise for the control of systems especially those systems that cannot be described mathematically or that are non-linear in nature. FLS may be viewed as a step toward a rapprochement between conventional precise mathematical control and human-like decision making.


  •  After completing this module, students will be expected to be able to:

    • Demonstrate an understanding of the principles of fuzzy logic
    • Explain how fuzzy logic can be applied to the design of intelligent machines
    • Demonstrate an understanding of hybrid fuzzy systems and their applications
    • Design a fuzzy controller for a an intelligent machine
    • Describe new developments in fuzzy logic such as Type-2 fuzzy systems

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