Fixed-Time Adaptive Deferred Constrained Control for a Flexible Manipulator With Saturation and Variable Learning Rate

In this paper, a fixed-time adaptive deferred constrained control strategy is proposed for a flexible single-link manipulator system with input saturation. Fuzzy Neural Networks are utilized to estimate the unknown dynamics of the flexible manipulator system as well as the errors caused by input saturation. To address output constraints imposed within a prescribed time period, a time-shift function and an adjusted barrier function are introduced. The system’s stability is rigorously proven using the direct Lyapunov method. Finally, numerical simulations and experimental results are presented to validate the effectiveness and superiority of the proposed control approach.