Slip control of a quarter car model based on type-1 fuzzy neural system with parameterized conjunctions

In conventional fuzzy modeling and control, to obtain an optimal fuzzy system, a commonly used approach is to tune the parameters of the membership functions. However, if the membership functions carry significant expert knowledge about the system, this may be lost or distorted during the optimization process. In order to prevent such a loss of valuable information, parameterized conjunction operators may be used and their parameters can be tuned instead. In this paper such an approach is adopted to optimize a type-1 fuzzy neural system (FNS), used for slip control of a Quarter Car Model (QCM). The simulation results presented indicate the efficacy of the approach in meeting the desired objectives even under noisy conditions.