Sampled-Data Robot Adaptive Control With Stabilizing Compensation

Abstract

This article addresses the stability and performance of dis cretized adaptive control algorithms for robotic manipulator control and the compensation of these algorithms for improved stability and tracking performance. The discretization of Slotine and Li's direct adaptive control algorithm results in a sampled- data system for which stability has not been guaranteed. By formulating the entire sampled-data system in continuous time, Lyapunov's direct method is used to determine the sta bility and to derive a nonlinear discrete-time compensating term. This compensator is added to a multirate discretization of Slotine and Li's (1987a) adaptive algorithm, to stabilize the sampled-data system. For sufficiently high gain, globally stable performance and a known bound on the norm of the fil tered error is proven. The stabilizing effect of the compensator and validity of the error bound predictions are demonstrated through simulation and implementation of 2-DOF manipulatior control.

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