Self-modified flexible prescribed performance control for a class of strict feedback systems with input saturation and actuator faults

A self-modified flexible prescribed performance control is proposed for a class of strict feedback nonlinear systems with input saturation, unmeasured states, and actuator faults. The proposed method introduces key functions to achieve flexible finite-time prescribed performance with global and semi-global properties by adjusting the initial value of a time-varying scaling function. In addition, this study proposes a novel finite-time auxiliary system to modify performance boundaries in the presence of input saturation, allowing for the attainment of self-modified flexible finite-time prescribed performance. To approximate unmeasured states, an RBF neural network state observer is introduced. Additionally, dynamic surface control is used to address the complexity associated with backstepping. The closed-loop system’s stability is demonstrated to be semi-globally practically finite-time stable. Comparative simulations are conducted to validate the proposed control scheme’s effectiveness.