Iterative learning control for discrete-time nonlinear systems subject to actuator faults

Abstract

This paper focuses on an iterative learning fault-tolerant control strategy for discrete-time affine nonlinear systems where actuator faults arbitrarily occur. First of all, the actuator fault is decomposed into multiplicative model and additive one. Then, the behaviors of both the faults-corrupted control signals from the actuator to the systems and the faults-free ones from the iterative learning controller to the actuator are analyzed from the statistical point of view. Meanwhile, sufficient conditions of convergence are derived by resorting to norm. Last, in order to verify the effectiveness and reliability of the proposed results, numerical experiments are carried out. Both theoretical analysis and simulations illustrate that the developed strategy is satisfactory in maintaining decent tracking accuracy of the controlled systems, even though faults randomly occur in actuator.

Keywords

• iterative learning control
• discrete-time nonlinear systems
• actuator faults
• mathematic expectation