https://scholar.dgist.ac.kr/handle/20.500.11750/58893 2026-04-04T13:58:09Z https://scholar.dgist.ac.kr/handle/20.500.11750/59957 Title: Physically Informed Sideslip Angle Estimation for Electric Vehicles Using Lateral Tire Force Sensors and a GPR-UKF Observer Author(s): Nam, Kanghyun; Wang, Yafei; Fujimoto, Hiroshi Abstract: This article presents a nonlinear sideslip angle estimation framework that directly incorporates lateral tire force measurements into an observer structure. To address the limitations of conventional approaches that rely on tire models and slip angle approximations, a physically grounded tire model is developed that features load-dependent cornering stiffness, relaxation dynamics, and time-varying parameter adaptation. Cornering stiffness is estimated via a regression-based method using only measurable signals, and a Gaussian process regression (GPR) model is introduced to estimate the front-rear cornering stiffness. The resulting estimates are integrated into an unscented Kalman filter (UKF) observer for robust sideslip angle estimation under nonlinear and transient conditions. The framework is experimentally validated using a full-scale vehicle equipped with in-wheel motors (IWMs) and lateral tire force sensors. Results confirm that the proposed UKF-based observer achieves accurate and stable sideslip angle estimation during aggressive maneuvers and across varying road surfaces. This approach enables high-fidelity, real-time state estimation for advanced driver-assistance and automated driving applications. 2026-02-28T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59327 Title: Road Environment Aware Control Framework for Steering Feel Generation in Steer-by-Wire Systems Author(s): Cheon, Dasol; Nam, Kanghyun; Oh, Sehoon Abstract: In steer-by-wire (SBW) systems, where the steering wheel and the tire are not physically connected, the steering feel is artificially generated regardless of road conditions. Typically, SBW systems generate steering feel based on steering angle to steering torque models to provide specific reaction torques in response to the driver's steering input. However, since the steering wheel is not mechanically connected to the tire, the driver cannot feel the road surface condition. This article proposes a novel control algorithm framework that can extract and transfer road surface information while still following the desired steering feel model. The steering feel generation control and road wheel control are integrated to achieve this goal. Specifically, we propose a reference steering model (RSM) for steering feel generation and bilateral control (BiC) for integrated steering wheel and road wheel control. This allows us to reflect the road surface condition without changing the steering feel model or identifying the road surface parameters. We validate the effectiveness of our proposed control through experiments using an SBW test vehicle. 2025-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57796 Title: Synchronous Control of a Dual-Motor Driving Rack and Pinion Module for Steer-by-Wire System Author(s): Chung, Insu; Oh, Sehoon; Nam, Kanghyun Abstract: Synchronous control of multi-motor systems (MMS) is attracting attention as a method to achieve the objectives of accurate operation, load distribution, vibration, and noise reduction while responding to the demands of large forces, high speed, etc. in industrial sites. The performance of synchronous control is evaluated by command tracking and synchronization between motors. In order to improve these two goals, the existing synchronous control structures mainly designed controllers in the case of command tracking, and synchronization between motors used a compensation method using synchronous error. However, this method makes it difficult to analyze or control the level of synchronous error. This paper presents a novel control structure that directly controls tracking commands and synchronous errors by setting the input and output mean and differences of MMS as control targets. The model applying the proposed structure can reduce the resonance due to the combined compliance of the MMS in the input and output relationship, which has been validated by the experimentally measured frequency response function. In addition, experiments were conducted to verify the effectiveness of synchronous control performance when the proposed control structure was applied. The algorithm was constructed using Matlab/Simulink, and the actual equipment was controlled using a data acquisition (DAQ) board. In order to compare the existing synchronous control structures with the proposed control structure, a model-based controller based on dynamic analysis was designed, and the results were derived through two commands. Through this, it was verified that the proposed control structure can improve synchronous control performance. © IEEE. 2024-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/56641 Title: Tire Normal Force Estimation Based on Integrated Suspension State Measurement Author(s): Cheon, Dasol; Choi, Wonhyeok; Nam, Kanghyun; Oh, Sehoon Abstract: The vertical tire force can be utilized to obtain information on the longitudinal and lateral force of the tire through the tire friction circle. This means that ride safety can be improved by using the longitudinal force that affects the vehicle’s driving performance and the lateral force that allows for stable cornering without slips. In this paper, we propose a vertical tire force estimation method using sensors that can be implemented in the vehicle. First, the issue of the observability of the tire force is investigated, then we introduce a tire force observer that utilizes the acceleration of the sprung and the displacement between the sprung and unsprung mass. In the proposed observer design, the change in the road surface is taken into consideration as a Gaussian random variable. In addition, a 1/5 scaled quarter car model is developed as an experimental apparatus to evaluate the proposed method, and the proposed method is validated through simulation and experiment. © ICROS, KIEE and Springer 2024. 2024-05-31T15:00:00Z