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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Videos

A novel approach for multistage inference fuzzy control.

Z M Yeh1, H P Chen

  • 1Inst. of Ind. Educ. & Technol., Nat. Taiwan Inst. of Technol., Taipei.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 8, 2008
PubMed
Summary

This study introduces a novel multistage fuzzy controller design that passes fuzzy logic consequences between stages. This approach, utilizing a performance index for rule base generation, significantly reduces design and computation time for complex control systems.

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Area of Science:

  • Control Systems Engineering
  • Fuzzy Logic Theory
  • Computational Intelligence

Background:

  • Traditional fuzzy controllers can face challenges in managing complex systems with multiple stages of inference.
  • Designing and optimizing fuzzy rule bases for such systems often involves lengthy development cycles.
  • Computational efficiency during real-time execution is crucial for practical control applications.

Purpose of the Study:

  • To propose a novel methodology for designing multistage inference fuzzy controllers.
  • To develop a general method for automatically generating fuzzy rule bases based on system performance.
  • To enhance computational efficiency by reducing execution-time operations.

Main Methods:

  • A multistage inference fuzzy controller architecture where the output of one stage serves as the input for the next.
  • A performance index-based approach for generating fuzzy rule bases, aiming to shorten the design cycle.
  • Precomputation of fuzzy value match-degrees to minimize runtime computational load.

Main Results:

  • The proposed method successfully reduced the design cycle time for the fuzzy controller.
  • Precomputing match-degrees significantly decreased the number of operations required during execution.
  • Simulation studies demonstrated the feasibility and effectiveness of the method on complex systems.

Conclusions:

  • The presented multistage inference fuzzy controller design methodology is effective and feasible.
  • The performance index-based rule generation and precomputation techniques offer substantial improvements in design and computational efficiency.
  • This approach provides a valuable tool for developing advanced fuzzy control systems for complex applications.