Adaptive Control based Friction Estimation for Tracking Control of Robot Manipulators

2024-09-08

arXiv.org

AI Summary

To address low accuracy and poor robustness in friction compensation for robotic manipulator trajectory tracking—particularly in torque-sensor-free scenarios—this paper proposes an adaptive friction modeling and estimation method. First, a linearly parameterized friction model is formulated to explicitly capture static friction, stick-slip behavior, and the Stribeck effect. Second, a backstepping-based adaptive estimator is designed with explicit deviation suppression to enhance parameter convergence and closed-loop stability. Third, stochastic Fourier excitation signals are introduced to ensure persistent excitation during identification. Experimental validation on the KUKA iiwa 14 platform demonstrates significantly reduced friction estimation errors. The method achieves high-precision trajectory tracking under both stochastic Fourier and hand-drawn reference trajectories, while exhibiting strong robustness and generalizability across diverse control strategies.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Adaptive control is often used for friction compensation in trajectory tracking tasks because it does not require torque sensors. However, it has some drawbacks: first, the most common certainty-equivalence adaptive control design is based on linearized parameterization of the friction model, therefore nonlinear effects, including the stiction and Stribeck effect, are usually omitted. Second, the adaptive control-based estimation can be biased due to non-zero steady-state error. Third, neglecting unknown model mismatch could result in non-robust estimation. This paper proposes a novel linear parameterized friction model capturing the nonlinear static friction phenomenon. Subsequently, an adaptive control-based friction estimator is proposed to reduce the bias during estimation based on backstepping. Finally, we propose an algorithm to generate excitation for robust estimation. Using a KUKA iiwa 14, we conducted trajectory tracking experiments to evaluate the estimated friction model, including random Fourier and drawing trajectories, showing the effectiveness of our methodology in different control schemes.

Problem

• Adaptive Control
• Robot Arm
• Friction Compensation

Innovation

• Adaptive Control
• Friction Estimation
• Robot Arm