Robust Adaptive Neural Network-Based Funnel Tracking Control of a Class of Perturbed Euler-Lagrange Systems

In this article, a robust adaptive neural network (NN)-based funnel tracking control method is investigated to address anti-perturbation issues in disturbed Euler-Lagrange (EL) systems. The NN-based approaches are used to approximate the unknown nonlinear functions, whereas adaptive techniques are employed to estimate disturbance bounds. Meanwhile, an adaptive compensation control scheme is introduced, leveraging neural networks to mitigate above effects of nonlinear functions and disturbances. Additionally, a control strategy resembling a funnel is devised to confine trajectory tracking errors within a predefined region and finite time despite persistent perturbations. The bounded stability of tracking errors in EL systems with prescribed performance is established using Lyapunov stability theory. Simulations are performed to confirm the efficacy of the control technology.