Inverse model-based iterative learning control on hysteresis in giant magnetostrictive actuator

摘要

This article presents a new iterative learning control algorithm based on inverse model to decrease the hysteresis-caused tracking error of a giant magnetostrictive actuator. The first iteration input is calculated by the inverse model of the giant magnetostrictive actuator system according to the desired output Compared to a standard iterative learning control algorithm-in which the first iteration input usually is proportional to the desired output-the proposed algorithm converges more rapidly. Performance of the approach is demonstrated both theoretically and experimentally. The experimental results show that the inverse model-based iterative learning control converges about twice as fast as standard iterative learning control and reduces the hysteresis-caused error of giant magnetostrictive actuator to 0.5% of the total displacement range, which is comparable to the noise level of sensor measurement. Two sets of model parameters were identified by 0-1.5 and 0-5 V major hysteresis loop, respectively, and evaluated. The best convergence rate is obtained with the former case.