OCTOBER 19-20, 2012 - YMCA University of Science & Technology

Proceedings of the National Conference on
Trends and Advances in Mechanical Engineering,
YMCA University of Science & Technology, Faridabad, Haryana, Oct 19-20, 2012
4. Fuzzy Logic control system for quarter-car model
In present research work, the aim is to provide maximum ride comfort to riders by minimizing the vibration
magnitude of passenger seat and automotive structure as far as possible by selecting and implementing the
suitable control system. Fuzzy logic is based on the fuzzy set theory put forward by Lotfi Zadeh [22-23] which
emerged as a powerful and popular tool for engineers & scientists for use in nonlinear dynamic systems through
implementation of human knowledge and practical experience. In present case the main objective of FLC design
is to achieve desired control performance related to suspension movement for the changing road and load
disturbances.
There are four main components of a FLC making it to work as per expert’s knowledge or designer’s process
requirements:
1. Fuzzification interface changes or modifies the input data from crisp or numerical values into fuzzy values for
interpretation and comparison in next processing.
2. Rule base contains the knowledge in the form of If-Then rules related to achieving or providing the desired or
best system performance.
3. Inference Engine selects the best control rule for the application to control the plant activities at the current
time.
4. Defuzzification interface converts the fuzzy results decided by the inference engine into real mathematical
values and supplies to the plant.
Fig. 6: Application of FLC controller in Quarter car model.
In present work Mamdani method is selected in fuzzy inference system whereas “max-min” inference method is
selected as aggregation operator, being mostly used and simplest method. For defuzzification stage, “centroid”
method is employed where “center of mass” of the output generates a numerical value i.e. transformation of
linguistic variables to crisp values.
The two input to the selected fuzzy logic controller are sprung mass velocity and the suspension velocity
(velocity difference between sprung and unsprung mass) while the controller output is the damping value (C ) of
the semi-active MR damper.
Fig. 7: Number of Inputs and Output for designing Fuzzy Inference System for FLC
300