Motion control of a nonlinear pneumatic actuating table by using self-adaptation fuzzy controller

Since the pneumatic system has compressibility and time-delay nonlinearity behaviors, especially, for a heavy-duty pneumatic actuating table, it is difficult to establish an appropriate mathematical model for the design of model-based controller. Although fuzzy logic control has model-free feature, it still needs a time consuming work for rules bank and fuzzy parameters adjustment. Here, a self-adaptation fuzzy controller (SAFC) is proposed to control the up-down motion of a four legs pneumatic actuating table. This intelligent control strategy combines an adaptive rule with fuzzy and sliding mode control algorithms. It has on-line learning ability to deal with the system time-varying and non-linear uncertainty coupling behaviors, and adjust the control rules parameters. Only eleven fuzzy rules are required for this MIMO pneumatic actuating table motion control and these fuzzy control rules can be established and modified continuously by on-line learning. The experimental results show that this intelligent control algorithm can effectively monitor the pneumatic table to track the specified motion trajectories.