Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments
- URL: http://arxiv.org/abs/2604.09323v1
- Date: Fri, 10 Apr 2026 13:48:42 GMT
- Title: Robust Adaptive Backstepping Impedance Control of Robots in Unknown Environments
- Abstract summary: This paper presents a Robust Adaptive Backstepping Impedance Control (RABIC) strategy for robots operating in contact-rich and uncertain environments.<n>We propose a backstepping-based adaptive impedance control scheme for the inner loop to track the reference impedance model.<n>To handle uncertainties, we employ a Taylor series-based estimator for system dynamics and an adaptive estimator for determining the upper bound of external forces.
- Score: 15.262365015328832
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a Robust Adaptive Backstepping Impedance Control (RABIC) strategy for robots operating in contact-rich and uncertain environments. The proposed control strategy considers the complete coupled dynamics of the system and explicitly accounts for key sources of uncertainty, including external disturbances and unmodeled dynamics, while not requiring the robot's dynamic parameters in implementation. We propose a backstepping-based adaptive impedance control scheme for the inner loop to track the reference impedance model. To handle uncertainties, we employ a Taylor series-based estimator for system dynamics and an adaptive estimator for determining the upper bound of external forces. Stability analysis demonstrates the semi-global practical finite-time stability of the overall system. To demonstrate the effectiveness of the proposed method, a simulated mobile manipulator scenario and experimental evaluations on a real Franka Emika Panda robot were conducted. The proposed approach exhibits safer performance compared to PD control while ensuring trajectory tracking and force monitoring. Overall, the RABIC framework provides a solid basis for future research on adaptive and learning-based impedance control for coupled mobile and fixed serially linked manipulators.
Related papers
- A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
arXiv Detail & Related papers (2026-08-11T01:51:08Z) - SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors [48.60897189520386]
We introduce the JEPA-style model for real-time quadrotor control.<n>We combine a latent dynamics model with a novel physics-inspired prober that maps frozen latents to interpretable state.<n>To reduce the dependence on expensive and unsafe real-world data collection, we develop a structured pipeline for automated dataset generation.
arXiv Detail & Related papers (2026-06-22T15:00:59Z) - Intelligent Control of Differential Drive Robots Subject to Unmodeled Dynamics with EKF-based State Estimation [28.37226750522601]
This work introduces a unified control and state estimation framework that combines a Lyapunov-based nonlinear controller and Adaptive Networks (ANN)<n>The proposed controller leverages the universal approximation property of neural networks to model unknown nonlinearities in real time.<n>To ensure robust state estimation, the EKF fuses inertial measurement unit (IMU) and odometry from monocular, 2D-LiDAR and wheel encoders.
arXiv Detail & Related papers (2026-03-16T07:44:28Z) - Spatiotemporal Tubes for Differential Drive Robots with Model Uncertainty [2.4919077201316067]
This paper presents a Spatiotemporal Tube (STT)-based control framework for differential-drive mobile robots with dynamic uncertainties and external disturbances.<n>The proposed framework is validated through simulation studies on a differential-drive robot and benchmarked against state-of-the-art methods.
arXiv Detail & Related papers (2025-12-05T07:43:23Z) - Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator [50.191655141020505]
Reinforcement Learning (RL) has demonstrated impressive capabilities in robotic control but remains challenging due to high sample complexity, safety concerns, and the sim-to-real gap.<n>We introduce Offline Robotic World Model (RWM-O), a model-based approach that explicitly estimates uncertainty to improve policy learning without reliance on a physics simulator.
arXiv Detail & Related papers (2025-04-23T12:58:15Z) - Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics [14.149584412213269]
We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance.
Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue.
To address the challenges for robots operating with completely unknown dynamics and disturbances, a learning-based robust dynamic controller is developed.
arXiv Detail & Related papers (2024-03-23T04:36:12Z) - Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks [0.24578723416255746]
In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability.
We propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy.
arXiv Detail & Related papers (2024-02-04T15:54:03Z) - Incorporating Recurrent Reinforcement Learning into Model Predictive
Control for Adaptive Control in Autonomous Driving [11.67417895998434]
Model Predictive Control (MPC) is attracting tremendous attention in the autonomous driving task as a powerful control technique.
In this paper, we reformulate the problem as a Partially Observed Markov Decision Process (POMDP)
We then learn a recurrent policy continually adapting the parameters of the dynamics model via Recurrent Reinforcement Learning (RRL) for optimal and adaptive control.
arXiv Detail & Related papers (2023-01-30T22:11:07Z) - Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control [46.81433026280051]
We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems.
Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
arXiv Detail & Related papers (2022-10-23T00:45:05Z) - Gaussian Process-based Min-norm Stabilizing Controller for
Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
We propose a novel compound kernel that captures the control-affine nature of the problem.
We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
arXiv Detail & Related papers (2020-11-14T01:27:32Z) - Risk-Sensitive Sequential Action Control with Multi-Modal Human
Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure.
Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control.
A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
arXiv Detail & Related papers (2020-09-12T02:02:52Z) - Reinforcement Learning for Safety-Critical Control under Model
Uncertainty, using Control Lyapunov Functions and Control Barrier Functions [96.63967125746747]
Reinforcement learning framework learns the model uncertainty present in the CBF and CLF constraints.
RL-CBF-CLF-QP addresses the problem of model uncertainty in the safety constraints.
arXiv Detail & Related papers (2020-04-16T10:51:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.