Self-tuning RISE controller with adaptive FCNN compensation for asymptotic tracking of uncertain dynamic systems

To enhance tracking capability of the robust integral of the sign of the error controller (RISE) structure, an RISE controller with time-varying gains is proposed in this paper, which is incorporated with feedforward estimation from a fully connected neural network (FCNN). Specifically, for uncertain Euler–Lagrange dynamic systems, two error filters are designed to obtain faster convergence for tracking errors. Then, an adaptive update law is constructed for adjusting FCNN weights online, so as to approximate unknown nonlinear component in dynamic systems. To efficiently track trajectories, a self-tuning RISE feedback controller is designed to decrease high gains and avoid chattering phenomenon. System stability is rigorously analyzed by utilizing Lyapunov technique and LaSalle–Yoshizawa corollary extension, and asymptotic tracking convergence is proved. Finally, regarding robotic mechanisms, comparative tests are conducted to validate tracking performance of the proposed strategy.