A Predefined Time Constrained Adaptive Fuzzy Control Method With Singularity-Free Switching for Uncertain Robots

In this article, an adaptive fuzzy logic system (FLS) predefined time tracking control technique is investigated for robot systems with constrained position errors. To this end, a practical predefined time control approach that integrates FLSs learning technique, predefined time stability criterion, tan-type constraint function, and the back stepping recursive design is constructed for the first time. Compared with finite/fixed-time control schemes, the upper bound of the convergence time no longer depends on initial conditions or complex design parameters. Instead, it can be predefined by making adjustments to a single relevant control parameter in advance. In order to avoid the violation of constraint boundaries by position tracking errors, a suitable tan-type barrier Lyapunov function is constructed, and rigorous stability is derived from constrained predefined time Lyapunov theory. The results demonstrate that the error signals converge to a small range near origin within the user-defined time. In addition, to compensate for unknown nonlinearity, a new adaptive update law associated with the convergence time control parameter is established. Simulations and experiments on the Baxter robot platform are carried out to confirm the validity and functionality of the devised controller.