Model-Free Prescribed-Time Control Under Input Amplitude and Rate Saturations for Uncertain Mechatronic Systems With Mismatched Disturbances

This brief considers a trajectory tracking control issue of n-DOF mechatronic systems subject to uncertainties, mismatched disturbances, and input amplitude and rate saturations. In practical mechatronic systems, mismatched disturbances and input amplitude and rate saturations are common phenomenon affecting control performance, which require the development of an advanced and high-performance control strategy. In this brief, a neural network observer (NNO)-based model-free prescribed-time saturated controller (NNO-MFPTSC) is designed which consists of ultra-local model-based adaptive RBF neural network observer, prescribed-time sub-controller, and auxiliary dynamic system. The proposed NNO-MFPTSC that overcomes mismatched disturbances achieves stabilization and convergence within predefined time and provides a torque with amplitude and rate saturations. After that, the stability and prescribed-time convergence are analyzed by using Lyapunov theorem. Finally, the co-simulation of 3-DOF PUMA 560 robotic manipulator based on SolidWorks and MATLAB is realized. The results compared to adaptive intelligent controller (AIC) are given to show the effectiveness and superiority of NNO-MFPTSC.