A hybrid Gaussian mutation PSO with search space reduction and its application to intelligent selection of piston seal grooves for homemade pneumatic cylinders

Abstract

To make the motion tracking control of the homemade pneumatic cylinder as accurate as possible, it is necessary to properly match the seal groove and seal ring on the piston to generate the appropriate friction. However, since the relationship between friction and motion control accuracy is not yet clear, and the friction affected by many factors cannot be accurately modeled, it is impossible to design the optimal seal groove with the highest possible motion control accuracy for pneumatic cylinder through theoretical calculation or simulation optimization. For this reason, an experimental optimization method is considered to select the optimal one from the six empirically designed seal grooves through a particle swarm optimization (PSO) algorithm. To cope with the shortcomings of slow convergence rate and the tendency to fall into local optimum of the basic PSO algorithm (BPSO), three improved PSO algorithms (HGMPSO-0, HGMPSO and HGMPSO-SSR) are successively proposed in this study. The first two improved algorithms are compared with other PSO variants on 23 benchmark functions tested, and the results show that HGMPSO has better overall performance. To significantly improve the search space search efficiency and make the algorithm converge faster, the search space contraction mechanism is introduced into the HGMPSO algorithm to form the HGMPSO-SSR algorithm. The experimental results show that the HGMPSO-SSR algorithm significantly outperforms other PSO variants used for comparison and successfully achieves intelligent selection of piston seal grooves for the designed homemade pneumatic cylinder.