Model Free Adaptive Iterative Learning Control for Tool Feed System in Noncircular Turning

The linear motor tool feed system is an important part in noncircular turning. In this paper, the compact form dynamic linearization based model-free adaptive iterative control scheme (CFDL-MFAILC) and the full form dynamic linearization based model-free adaptive iterative control method scheme (FFDL-MFAILC) are designed for a complex nonlinear tool feed system. Theoretical analysis shows that the proposed scheme guarantees the output tracking error monotonic convergence along the iteration axis, and the FFDL-MFAILC is a complement and improvement to the CFDL-MFAILC. The designed control schemes are compared with PID and iterative learning feedforward and model-free adaptive predictive control feedback combination scheme (ILC-MFAPC) by simulations and experiments. Simulation results show that the proposed scheme can greatly decrease linear motor position error as iteration time increase, and has better position control advantages then other algorithms, the FFDL-MFAILC has faster convergence speed and smaller steady-state error than the CFDL-MFAILC. Experiment results prove that the proposed scheme is effective in linear motor tool feed system position control.