Adaptive designated-time fuzzy event-triggered tracking of constrained nonlinear systems and its applications in robotic systems

This paper develops a designated-time adaptive tracking control strategy for nonlinear systems subject to multiple sources of uncertainty, including output constraints, unmodeled dynamics, and unknown disturbances. By incorporating an enhanced fuzzy logic system for parameter estimation and a bounded command filtering approach, this work proposes a systematic tracking control scheme that effectively avoids the issue of computational complexity explosion. To address the significant uncertainties induced by constraints, an asymmetric barrier Lyapunov function is used for analysis and design under the condition of known control coefficients. Furthermore, a controller constructed based on an event-triggered mechanism ensures uniform boundedness and designated-time convergence of all signals. The feasibility and effectiveness of the proposed control method are validated through a practical application case.