Neural network-based adaptive fault-tolerant control for nonlinear systems with unknown backlash-like hysteresis and unmodeled dynamics

This paper explores adaptive neural fault-tolerant control for nonlinear systems characterized
by a nonstrict-feedback structure, tackling the difficulties arising from unmodeled dynamics
and unknown backlash-like hysteresis. A dynamic signal is introduced to mitigate the adverse
effects of unmodeled dynamics, while radial basis function neural networks (RBFNNs) are
utilized to capture the unknown nonlinear uncertainties presented in the system. Furthermore,
the impact of unknown hysteresis input is compensated for by approximating an intermediate
variable. By employing the backstepping technique along with neural network approximations,
an adaptive neural fault-tolerant control scheme is developed. Through the application of
Lyapunov stability theory, the proposed control strategy guarantees the boundedness of all
signals within the closed-loop system and ensures that the tracking error meets the specified
performance criteria, even in the presence of challenges such as unmodeled dynamics, unknown
backlash-like hysteresis, and actuator faults. Two illustrative examples are included to showcase
the effectiveness of the proposed control scheme.