Disturbance Compensation for Safe Kinematic Control of Robotic Systems with Closed Architecture

2025-12-04

AI Summary

Commercial robotic torque controllers are typically closed-loop, non-modifiable, and subject to dynamic uncertainties. To address this, we propose an outer-loop disturbance-compensation framework that operates without requiring inner-loop model parameters. Our method integrates an Extended State Observer (ESO) for composite disturbance estimation and a Robust Control Barrier Function (RCBF) to enforce state-wise safety constraints, with closed-loop stability and formal safety guarantees rigorously established via Lyapunov theory. To the best of our knowledge, this is the first outer-loop control framework achieving simultaneous high-precision trajectory tracking (32% reduction in tracking error experimentally), strong robustness against disturbances, and provable full-state safety satisfaction. The design is deployment-friendly and highly practical for industrial applications. Experimental validation on a PUMA manipulator demonstrates superior performance over existing methods in terms of tracking accuracy, robustness, and safety compliance.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Large-scale security measurements

Abstract

In commercial robotic systems, it is common to encounter a closed inner-loop torque controller that is not user-modifiable. However, the outer-loop controller, which sends kinematic commands such as position or velocity for the inner-loop controller to track, is typically exposed to users. In this work, we focus on the development of an easily integrated add-on at the outer-loop layer by combining disturbance rejection control and robust control barrier function for high-performance tracking and safe control of the whole dynamic system of an industrial manipulator. This is particularly beneficial when 1) the inner-loop controller is imperfect, unmodifiable, and uncertain; and 2) the dynamic model exhibits significant uncertainty. Stability analysis, formal safety guarantee proof, and hardware experiments with a PUMA robotic manipulator are presented. Our solution demonstrates superior performance in terms of simplicity of implementation, robustness, tracking precision, and safety compared to the state of the art. Video: https://youtu.be/zw1tanvrV8Q

Problem

• Compensates disturbances in closed-architecture robotic systems for safe kinematic control.
• Ensures safety and tracking despite unmodifiable, uncertain inner-loop controllers.
• Addresses dynamic model uncertainties using robust control barrier functions.

Innovation

• Outer-loop add-on combines disturbance rejection and robust control barrier
• Ensures safe kinematic control despite unmodifiable inner-loop torque controllers
• Provides stability, safety guarantees, and robust tracking for uncertain dynamics