Enhanced Robust Motion Control based on Unknown System Dynamics Estimator for Robot Manipulators

TL;DR

This work tackles precise manipulation of high-DoF robot manipulators under unknown disturbances by introducing an Unknown System Dynamics Estimator (USDE) that does not require joint acceleration measurements or the inverse inertia matrix. Building on USDE, two robust controllers are proposed: USDE-AG with adaptive feedback gains and USDE-ST with a super-twisting sliding mode design; both aim to accelerate error convergence and enhance disturbance rejection. The authors provide theoretical stability analyses, showing exponential convergence for USDE-AG and USDE-FG, and finite-time convergence for USDE-ST, and validate the approaches on a 7-DoF Franka Panda, demonstrating superior tracking, robustness to unknown payloads, and controllable chattering. The methods enable effective deployment of advanced disturbance-aware control on high-dimensional robots, with potential impact on precision manipulation in uncertain environments.

Abstract

To achieve high-accuracy manipulation in the presence of unknown disturbances, we propose two novel efficient and robust motion control schemes for high-dimensional robot manipulators. Both controllers incorporate an unknown system dynamics estimator (USDE) to estimate disturbances without requiring acceleration signals and the inverse of inertia matrix. Then, based on the USDE framework, an adaptive-gain controller and a super-twisting sliding mode controller are designed to speed up the convergence of tracking errors and strengthen anti-perturbation ability. The former aims to enhance feedback portions through error-driven control gains, while the latter exploits finite-time convergence of discontinuous switching terms. We analyze the boundedness of control signals and the stability of the closed-loop system in theory, and conduct real hardware experiments on a robot manipulator with seven degrees of freedom (DoF). Experimental results verify the effectiveness and improved performance of the proposed controllers, and also show the feasibility of implementation on high-dimensional robots.

Figures (5)

• Figure 1: Three USDE-based robust motion control schemes for robot manipulators to handle unknown disturbances in manipulation tasks.
• Figure 2: Plots showing experiment setup. The Franka robot is manipulating an unmodeled 1 kg blue dumbbell. (a) and (b) are experimental snapshots of no-payload and unmodeled-payload motion stages, respectively. (c) gives the desired joint trajectory $\bm{q}^{des}$ mapped from Cartesian space.
• Figure 3: Experimental results of four motion control schemes: CTC (blue), USDE-FG (red), USDE-AG (yellow) and USDE-ST (green). From left to right, the columns represent the measured joint position $\bm{q}$, the joint tracking error $\bm{e}$ (rough and detailed), the measured joint torque $\bm{\tau}$ and the disturbance estimation $\hat{\bm{d}}$. From top to bottom, each row denotes the states of each joint.
• Figure 4: Adaptive control gain $\hat{\bm{\mathcal{K}}}$ of the USDE-AG controller
• Figure 5: Comprehensive comparison of four controllers. The USDE-ST performs best in terms of tracking accuracy and system robustness.