Preset-trajectory-based output-feedback design for adaptive decentralized prescribed-time tracking of uncertain...

This paper develops a preset-trajectory-based adaptive output-feedback strategy for decentralized prescribed-time tracking of uncertain strict-feedback nonlinear systems subject to interconnections and dead-zone inputs. The framework assumes that unmatched nonlinear functions, nonlinear interconnection terms, and the parameters of dead-zone input nonlinearities are completely unknown. The primary contributions include designing local tracking-error trajectories (i.e., local preset trajectories) using local output-feedback signals and developing a novel design strategy with a time-varying function to initialize parameter estimation errors to zero without requiring zero initial estimates. Neural-network-based state filters reconstruct the unmeasured states, while an adaptive dead-zone inverse approximation compensates for the unknown dead-zone nonlinearity. A decentralized output-feedback controller is designed to achieve practical prescribed-time stability, with transient behavior shaped by the constructed preset trajectories. The proposed design explicitly avoids the singularity that may arise in the adaptive dead-zone inverse approximation due to parameter estimates approaching zero. This work rigorously analyzes the boundedness of the closed-loop signals and practical prescribed-time stability of the local tracking errors, based on the zero initial condition of the Lyapunov function. The simulation results comparing the proposed approach with existing methods demonstrate its effectiveness and advantages.