Adaptive Composite Observer-Based Global Finite Time Control With Prescribed Performance for Robots

As humans focus control only on task-space variables to achieve dexterous manipulation, robots could strongly profit from advanced task-space control, which is still blocked by kinematic and dynamic uncertainties now. This article proposes an adaptive composite observer (ACO) with finite-time sliding-mode manifold to compensate uncertain kinematics and dynamics synthetically, then develops a novel nonsingular terminal sliding mode controller based on an adaptive neural network (NN) to stabilize the task-space tracking errors directly with prescribed performance. The global finite-time stability of the entire observer-controller system is achieved by Lyapunov method, and the observer and controller errors will converge to zeros in finite time with preassigned transient and steady-state performance. Simulation and experimental studies are also presented to verify the effectiveness of the designed control methods.