Identification of pneumatic cylinder friction parameters using genetic algorithms

A method for identifying friction parameters of pneumatic actuator systems is developed in this paper, based on genetic algorithms (GA). The statistical expectation of mean-squared errors is traditionally used to form evaluation functions in general optimization problems using GA. However, it has been found that, sometimes, this type of evaluation function does not lead the algorithms to have a satisfactory convergence, that is, the algorithm takes a long period of time or fails to reach the values of parameters to be identified. Different evaluation functions are, therefore, studied in the paper and two types of evaluation functions are found to have the expected rate of convergence and the precision. The algorithm is initially developed and tested using the benchmark data generated by simulations before it is applied for parameter identification using the data obtained from the real system measurement. The results obtained in the paper can provide the manufacturers with the observation to the characteristics inside the pneumatic cylinders.