Concurrent Learning-Based Adaptive Control of an Uncertain Robot Manipulator With Guaranteed Safety and Performance

This article investigates the tracking problem of an uncertain $n$ -link robot manipulator with guaranteed safety and performance. To tackle parametric uncertainties, torque filtering-augmented concurrent learning (CL) method is introduced for online identification unknown system without requirements joints acceleration. By using CL, parameter convergence by exploiting current historical data simultaneously. technique enjoys practicability compared common methods that need to incorporate external noises satisfy persistence excitation condition convergence. Based on estimated model, we design a barrier Lyapunov function (BLF)-based adaptive control law backstepping analysis. ensuring boundness BLF, output error are proved lie in set performance set, respectively. Numerical simulation results experiment tests validate proposed strategy.