A Kalman Filter-Based Disturbance Observer for Steer-by-Wire Systems

2025-12-29

AI Summary

To address high-frequency disturbances induced by driver’s involuntary torque (i.e., drive impedance) that degrade steering performance in steer-by-wire systems, this paper proposes a disturbance observer relying solely on motor state measurements. The driver’s passive torque is innovatively modeled as a first-order PT1-type extended state and integrated into both linear and nonlinear system models. For friction-induced nonlinearity, an adaptive Extended Kalman Filter (EKF)-based observer is designed. Unlike conventional approaches requiring expensive torque sensors or suffering from low temporal resolution, the proposed method achieves ultra-low latency (14 ms) and accurate high-frequency disturbance reconstruction. During stick-slip transitions, EKF estimation accuracy significantly outperforms that of the Linear Kalman Filter (LKF). Comprehensive simulations validate both the effectiveness and engineering feasibility of the approach.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Steer-by-Wire systems replace mechanical linkages, which provide benefits like weight reduction, design flexibility, and compatibility with autonomous driving. However, they are susceptible to high-frequency disturbances from unintentional driver torque, known as driver impedance, which can degrade steering performance. Existing approaches either rely on direct torque sensors, which are costly and impractical, or lack the temporal resolution to capture rapid, high-frequency driver-induced disturbances. We address this limitation by designing a Kalman filter-based disturbance observer that estimates high-frequency driver torque using only motor state measurements. We model the drivers passive torque as an extended state using a PT1-lag approximation and integrate it into both linear and nonlinear Steer-by-Wire system models. In this paper, we present the design, implementation and simulation of this disturbance observer with an evaluation of different Kalman filter variants. Our findings indicate that the proposed disturbance observer accurately reconstructs driver-induced disturbances with only minimal delay 14ms. We show that a nonlinear extended Kalman Filter outperforms its linear counterpart in handling frictional nonlinearities, improving estimation during transitions from static to dynamic friction. Given the study's methodology, it was unavoidable to rely on simulation-based validation rather than real-world experimentation. Further studies are needed to investigate the robustness of the observers under real-world driving conditions.

Problem

• Estimates high-frequency driver torque without costly sensors
• Addresses driver impedance degrading steer-by-wire performance
• Improves disturbance estimation in nonlinear friction conditions

Innovation

• Kalman filter-based observer estimates high-frequency driver torque
• Uses PT1-lag approximation to model driver impedance as extended state
• Nonlinear extended Kalman Filter handles frictional nonlinearities effectively