A Neural Network-Based Prescribed-Time Controller Formulation with Update Modularity for a Class of Nonlinear Systems

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.