Observer-Based Gate Recurrent Learning for Model-Free Control of 6-DoF Manipulators: Considerations for Input Saturation and Output Constraints

This article investigates the gate recurrent unit neural network (GRUNN)-based learning control framework for the online tracking of manipulators with guaranteed prescribed performance and the absence of velocity measurements. In contrast with methods requiring dynamics modeling, the proposed scheme deals with completely unknown manipulator dynamics, input saturation, and disturbances by virtue of a modified GRUNN. First, the guaranteed output bounds are used to achieve prescribed performance based on the barrier Lyapunov function (BLF). Then, a high-gain observer (HGO) is designed to observe the velocities and construct output feedback control. Finally, a Lyapunov-based GRUNN learning controller is derived by utilizing the backstepping method, in which the GRUNN approximates real-time lumped uncertainties. The advantages are that the proposed solution not only guarantees the prescribed performance with the least sensors but also can handle model-free situations. Indispensably, the practical uniform ultimate boundedness of the closed-loop system is proved. Both static and dynamic tracking tests are performed. Experiment results indicate that the tracking precision is improved, which convincingly demonstrates the significant online estimation performance of the novel GRUNN and the superiority of the proposed control framework.