Adaptive Model-Base Control of Quadrupeds via Online System Identification using Kalman Filter

TL;DR

This paper introduces an online system identification method using a Kalman filter for quadruped robots, enabling adaptive control with variable payloads and improved robustness and tracking performance.

Contribution

It presents a novel Kalman filter-based approach for real-time identification of robot mass and COM, enhancing model-based control adaptability.

Findings

More robust to measurement noise than RLS methods

Improves tracking performance with payload variations

Enables adaptive control in real-time

Abstract

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.