Two-Time-Scale Composite Learning Online Identification and Control for Compliant-Joint Robots

Abstract

SP-based synthesis yields two-time-scale control that allows compliant-joint robots to achieve high-quality tracking at low implementation cost. Composite learning enables exact online identification and control of robots without the stringent condition known as persistent excitation (PE). However, to achieve exact online identification for compliant-joint robots, parameter update derived from SP-based synthesis and composite learning requires physically unavailable states. This paper presents a novel SP-based composite learning robot control (SP-CLRC) strategy for compliant-joint robots that achieves exact online identification and control without requiring access to physically unavailable states. In the proposed method, link-side and actuator-side parameters are estimated separately, enabling exact online identification using available robot states. A two-time-scale composite learning method is proposed to guarantee practical exponential stability of the closed-loop system with parameter convergence under interval excitation, a condition strictly weaker than PE. Experiments on a two-degree-of-freedom robot driven by series elastic actuators have shown that the proposed SP-CLRC significantly outperforms the baseline in online identification and tracking accuracy.