ESNO-based predefined-time trajectory tracking control for mobile manipulator with full-state constraints

In this study, the problem of adaptive predefined-time trajectory tracking for mobile manipulator (MMR) subject to external disturbances and full-state constraints is addressed. Firstly, a novel ESN observer (ESNO) with guaranteed predefined-time convergence is proposed, incorporating an adaptive error compensation mechanism to enhance disturbance estimation accuracy. Secondly, an interval-based asymmetric prescribed performance control strategy with a tunable predefined-time prescribed performance function is developed, and its simplified structure reduces computational burden. Thirdly, a predefined-time adaptive filter is designed to avoid the “complexity explosion” problem. Finally, Lyapunov stability analysis verifies predefined-time convergence of all errors, while numerical simulations and physical MMR platform experiments verify the feasibility and superiority of the algorithm.