Model-Free Adaptive Iterative Learning Control Based on Data-Driven for Noncircular Turning Tool Feed System

In practical applications, noncircular turning tool feed system repeat the same control tasks over a finite time interval. But it does not have the ability to improve the tool position error from past repeated operations. This paper will use Partial Form Dynamic Linearization based Model-Free Adaptive Iterative Learning Control (PFDL-MFAILC) algorithm in noncircular turning tool feed. PFDL-MFAILC is a data-driven iterative learning control algorithms. The design of noncircular turning tool feed controller is just rely on input and output data. Simulation of PFDL-MFAILC algorithm show that the noncircular turning tool feed position error is improved as the number of repetitions increases. By contrast with PID control algorithm, position tracking accuracy of PFDL-MFAILC algorithm is significantly better than traditional PID control algorithm. After 60 iterations, the steady-state error of PFDL-MFAILC algorithm is much lower than the steady-state error of the PID algorithm. PFDL-MFAILC algorithm achieve the goal that improving position precision of noncircular turning tool feed and making noncircular turning tool feed have self-learning ability from past repeated operations.