Multi-dimensional Taylor Network-Based Fault-Tolerant Control for Nonlinear Systems with Unmodeled Dynamics and Actuator Faults

This work investigates the problem of Multi-dimensional Taylor Network (MTN)-based fault-tolerant control (FTC) for single-input and single-output nonlinear systems in non-strict feedback form. A MTN-based FTC method is presented for nonlinear systems with actuator faults and unmodeled dynamics. The actuator faults are contains both the loss of effectiveness factor of the actuator and a time-varying bias signal. MTN is used to approximate the unknown nonlinear functions, while unmodeled dynamics and dynamical disturbances are handled with the help of dynamical signal functions. A systemically backstepping-based fault-tolerant control scheme is proposed based on Lyapunov stability theory and MTN approximation ability. The suggested technique ensures that all closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small region around the origin. To demonstrate the effectiveness of the proposed controller design, three examples, including a single-link robot manipulator, are presented.

Automated summary

This paper investigates the problem of Multi-dimensional Taylor Network (MTN)-based fault-tolerant control (FTC) for single-input and single-output nonlinear systems in non-strict feedback form. A MTN-based FTC method is presented for nonlinear systems with actuator faults and unmodeled dynamics. The proposed technique ensures that all closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small region around the origin. Three examples, including a single-link robot manipulator, are presented to demonstrate the effectiveness of the proposed controller design.