A Neural Network-Based Prescribed-Time Controller Formulation with Update Modularity for a Class of Nonlinear Systems
Bildiriler > Tam Metin Bildiri · 2025
Yazarlar: Hüseyin Deniz ÖZTÜRK, Enver TATLICIOĞLU, Erkan Zergeroglu
11th International Conference on Control Decision and Information Technologies-CODIT, Hırvatistan, Split, Uluslararası, Sözlü Sunum, Tam Metin Bildiri, 2025-2025, ss 1149-1152
DOI: 10.1109/CoDIT66093.2025.11321718
Özet
This work concentrates on a neural network-based, prescribed time controller formulation for a class of nonlinear systems having parametric uncertainty. The aim of the controller is to ensure that the tracking error converges to the origin within a user-defined prescribed time despite the presence of bounded disturbances and parametric uncertainties with controller/update law modularity. The stability of closed-loop error system has been ensured via Lyapunov-based arguments. Numerical simulations are conducted to illustrate the feasibility of the proposed method.