IMPROVED FUZZY SLIDING-MODE CONTROL FOR A LINEAR SERVO MOTOR SYSTEM

In this paper, a novel neural network is first used to learn the dynam ics of the proposed linear servo motor system. Then a modified zero-ph ase tracking controller (MZPTC) based on this learning model is design ed to obtain an acceptable tracking result. Because an MZPTC involves open-loop control, its performance cannot be assured, as the system is subjected to external loads or the aging of system components. To ove rcome this difficulty, an adaptation to the control input generated by a fuzzy sliding-mode controller (FSMC) is then synthesized with the p revious MZPTC. Hence, improved control performance (i.e., good traject ory tracking under uncertain conditions, the reduction of control effo rt as compared with an FSMC, and the ease of constructing a fuzzy tabl e for a specific system performance), is achieved. The experimental pe rformance of the proposed hybrid control technique (or so-called ''imp roved fuzzy sliding-mode control'' (IFSMC)) is compared with those of the MZPTC and FSMC.