Two-Time-Scale Composite Learning Online Identification and Control for Compliant-Joint Robots
Tian Shi, Lin Liu, Qian Wang, Jinya Su, Shihua Li, Yongping Pan
AI summary
Problem
Existing SP-based composite learning control for compliant-joint robots relies on physically inaccessible states for exact identification, degrading accuracy, and requires the stringent persistent excitation condition.
Approach
The authors introduce a two-time-scale composite learning control strategy that separately estimates link-side and actuator-side parameters using only available robot states, guaranteeing stability under the weaker interval excitation condition.
Key results
- Separates link-side and actuator-side parameter estimation using only measurable states
- Achieves exact online identification and parameter convergence under interval excitation
- Guarantees practical exponential stability of the closed-loop system
- Demonstrates significantly superior tracking accuracy and identification performance over baselines in SEA-driven robot experiments
Why it matters
Enables robust, high-performance control of compliant-joint robots in real-world applications where full state measurement is impossible and excitation conditions are limited.
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 param- eters 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.