Disturbance Observer-Based Adaptive Chainlike Filter Approach for Prescribed-Time Consensus Tracking of Nonlinear Multiagent Systems via Dynamic State and Input Triggering.

This article addresses the problem of adaptive prescribed-time distributed consensus tracking with dynamic full-state and input triggering for a class of uncertain state-constrained strict-feedback multiagent systems with external disturbances. The primary contribution lies in developing of a novel prescribed-time disturbance observer-based adaptive chainlike filter, capable of generating smooth estimates of intermittently triggered state-feedback signals while compensating for external disturbances and unknown nonlinearities within a predefined convergence time. The multiagent systems are nonlinearly transformed to address state constraints, without needing feasibility conditions on virtual control laws in the recursive design. The dynamic triggering variables are introduced using a prescribed-time adjustment function and distributed tracking errors. Based on the state variables of the adaptive chainlike filters, a prescribed-time distributed consensus tracking strategy is established to guarantee the prescribed-time convergence of filtering errors, disturbance observation errors, leader estimation errors, and consensus tracking errors, without requiring continuous state-feedback measurements. The shared use of neural networks across chainlike filters, disturbance observers, and controllers reduces computational complexity. The practical prescribed-time stability and satisfaction of state constraints in the closed-loop system are proven through a rigorous technical lemma. Finally, simulation results validate the effectiveness and robustness of the proposed control scheme.