Adaptive Model-Base Control of Quadrupeds via Online System Identification using Kalman Filter
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916795935358976 |
|---|---|
| author | Haack, Jonas Stark, Franek Vyas, Shubham Kirchner, Frank Kumar, Shivesh |
| author_facet | Haack, Jonas Stark, Franek Vyas, Shubham Kirchner, Frank Kumar, Shivesh |
| contents | Many real-world applications require legged robots to be able to carry variable payloads. Model-based controllers such as model predictive control (MPC) have become the de facto standard in research for controlling these systems. However, most model-based control architectures use fixed plant models, which limits their applicability to different tasks. In this paper, we present a Kalman filter (KF) formulation for online identification of the mass and center of mass (COM) of a four-legged robot. We evaluate our method on a quadrupedal robot carrying various payloads and find that it is more robust to strong measurement noise than classical recursive least squares (RLS) methods. Moreover, it improves the tracking performance of the model-based controller with varying payloads when the model parameters are adjusted at runtime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Model-Base Control of Quadrupeds via Online System Identification using Kalman Filter Haack, Jonas Stark, Franek Vyas, Shubham Kirchner, Frank Kumar, Shivesh Robotics Many real-world applications require legged robots to be able to carry variable payloads. Model-based controllers such as model predictive control (MPC) have become the de facto standard in research for controlling these systems. However, most model-based control architectures use fixed plant models, which limits their applicability to different tasks. In this paper, we present a Kalman filter (KF) formulation for online identification of the mass and center of mass (COM) of a four-legged robot. We evaluate our method on a quadrupedal robot carrying various payloads and find that it is more robust to strong measurement noise than classical recursive least squares (RLS) methods. Moreover, it improves the tracking performance of the model-based controller with varying payloads when the model parameters are adjusted at runtime. |
| title | Adaptive Model-Base Control of Quadrupeds via Online System Identification using Kalman Filter |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.13432 |