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Semi-active suspension has been widely applied in commercial vehicle suspension in order to get good riding comfortableness. Fuzzy logic control (FLC) has been widely applied in the field of kinetic control because control rule of FLC is easy to understand. But the gain of fuzzy rules and adjustment of membership functions usually depend on experts' experiences and repeated experiments, thus the fuzzy rules and membership functions has strong subjectivity, also are easily affected by environment of experiments, so the main problem of fuzzy logic controller design is selection and optimization of fuzzy rules and membership functions. Genetic Algorithms (GA) is the algorithm that searches the optimal solution through simulating natural evolutionary process and is one of the evolution algorithms which have most extensive impact.

Speed ratio and clamping force are two of the metal-belt CVT control targets. Conventional control strategies can not correspond to the driver's intention or provide various driving environment. A fuzzy logic ratio control algorithm and a fuzzy logic clamping force control algorithm for a metal-belt CVT are proposed. Nevertheless, high-quality fuzzy control rule base and factors of FLC are difficult to gain because repeated tests and experts' experience are needed. Therefore, genetic algorithm (GA) is introduced to optimize the fuzzy control algorithms. Using the optimized fuzzy control algorithms, Metal-belt CVT control simulations were implemented. The results show that a faster response and better robustness can be gained when compared with those of the PID control.