Advanced control of active magnetic bearings with learning control schemes

Abstract

This thesis deals with the unbalance compensation of active magnetic bearings. Time-domain iterative learning control (ILC) scheme and automatic learning control (ALC) scheme are proposed to compensate the unbalance force. ALC is based on time-domain ILC and gain-scheduled control. The controller can obtain the optimized control current for unbalance compensation by iterative learning. In addition, variable learning cycle and variable learning gains are employed in the learning law for better performance against rotational speed fluctuation and ability to work at different speeds. The effectiveness of proposed time-domain ILC and ALC schemes is tested through simulations and experiments. Their performances are compared and the results show that both ILC and ALC have excellent performance during constant speed test. In addition, ALC presents better performance during speed fluctuation and it is effect over a wide range of operation speeds.