Approximation-Free Robust Tracking Control of Unknown Redundant Manipulators With Prescribed Performance and Input Constraints

Abstract

This article proposes a novel neural control architecture that employs input-output information to compensate for the lack of knowledge about the robot model to achieve prescribed tracking performance in the presence of joint constraints. To this end, an observer-controller zeroing neural network framework is formulated that combines online estimation of the unknown model’s Jacobian with a trajectory tracking controller that implements joint angle and velocity constraints via a nonlinear map. Further, prescribed performance constraints are embedded within this architecture to achieve desired transient and steady-state performance along with added robustness to chattering. Hence, in comparison to prior studies, the proposed scheme facilitates a more robust control architecture with the added benefits of more stringent application of the input constraints and superior transient and steady-state performance. Simulation and experimental studies of trajectory tracking, including comparisons with leading alternative designs, are used to verify the efficacy and superior performance of the proposed scheme.