Actuator fault-tolerant iterative learning control of the magnetic brake system

Abstract

Design of fault-tolerant control for nonlinear systems is usually focused on maxi-mizing some criteria defining the level of robustness with respect to potential faults without significant degradation of the system performance. Therefore, strong interest is produced by compromise approaches which would generate decent control quality for the broadest possible class of potential faulty scenarios. Here, an approach is proposed with the application to magnetic brake system making use of the repetitive character of the control task. The concept of iterative learning control driven by measurement data is utilized to properly update the control signal in order to adapt to possible actuator fault states. A learning controller is adopted build on a mixture of neural networks for various operating points. Thus, it is able to adapt to changing working conditions of the device. Moreover, using the procedure employing an ensemble of inverse models, actuator faults can be successfully accommodated. The paper provides the complete iterative learning procedure including the system identification, fault estimation and fault-tolerant control. The numerical example on the reference tracking problem for magnetic brake system is discussed, taking into account various faulty scenarios.