Observer-Based Model-Free Iterative Learning for Fault-Tolerant Control of Nonlinear Systems

This paper proposes an observer-based model-free iterative learning fault tolerant control (ObMFilFTC) algorithm for the nonlinear system with disturbances and non-repetitive time-varying actuator faults. First, an original linearization data model (LDM) considering non-repetitive uncertainties is established. Since it contains fault information, this allows the fault information to be estimated using the parameter estimation law. The external disturbances and the non-repetitive time-varying actuator faults constitute the total non-repetitive uncertainties. Next, to deal with non-repetitive uncertainties, we present a novel iterative output observer (ILO) that considers all historical iteration observation errors to estimate inaccurate outputs ruined by non-repetitive uncertainties. With the introduction of ILO, the tracking accuracy and the ability to suppress non-repetitive uncertainties are improved. Additionally, the inclusion of the tracking error integral term in the ILO enhances the convergence speed. Meanwhile, by utilizing the estimated outputs, an observer-based parameter updating law is proposed. Furthermore, we propose an optimal iterative learning control (ILC) algorithm to ensure precise tracking of the desired trajectory. The convergence of the proposed ObMFilFTC method is proofed strictly. The proposed ObMFilFTC method guarantees that the system can follow the desired trajectory despite non-repetitive actuator faults and disturbances in nonlinear systems, relying solely on input/output(I/O) data. Finally, the simulation results further demonstrate the effectiveness of the proposed algorithm.