Robust fuzzy-neural-network control for two-axis motion control system based on TMS320C32 control computer

In this study, a robust fuzzy-neural-network (RFNN) sliding-mode control based on computed-torque control design for a two-axis motion control system in which the X-Y table is composed of two permanent magnet linear synchronous motor (PMLSM) is proposed. First, a single-axis motion dynamics with the introduction of a lumped uncertainty including cross-coupled interference between the two-axis mechanism is derived. Then, to improve the control performance in reference contours tracking, the RFNN sliding-mode control system is proposed to effectively approximate the equivalent control of the sliding-mode control method based on the derived motion dynamics. Moreover, the motions at X-axis and Y-axis are controlled separately. Using the proposed control, the motion tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled interference and friction force can be obtained as well. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The experimental results due to circle and four leaves reference contours show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.