https://scholar.dgist.ac.kr/handle/20.500.11750/195 2026-08-27T01:49:09Z https://scholar.dgist.ac.kr/handle/20.500.11750/60614 Title: Nut-Driven Transmission Force Controllable Motion Platform for Suppressing Low-Frequency and Impulsive Base Disturbances Author(s): Cheon, Dasol; Oh, Sehoon Abstract: This article proposes a transmission-force controllable actuator (TFCA) for vibration suppression in linear motion platforms. Unlike conventional screw-driven systems, the nut-driven TFCA integrates a compliant spring and encoder to measure and control the transmission force in real time, thereby reducing reflected inertia, increasing stiffness, and enabling rapid response to external disturbances. Dynamic models were derived, and two control strategies-acceleration-based vibration suppression control (AVSC) and transmission-force and ACSC (TAVSC)-were implemented. Experiments using a base-shaker setup evaluated three scenarios: back-drivability, impulse disturbance, and multisine excitation (0.1-100 Hz). Results show that TAVSC reduced acceleration and jerk by over 50% in the time domain and achieved more than 90% suppression in the 0.5-8 Hz band, while AVSC exhibited only marginal improvement. Under multisine excitation, TAVSC maintained more than 5 dB suppression across the spectrum and 86% reduction in the 0.5-8 Hz band. These findings confirm that transmission-force feedback substantially improves vibration suppression against impulsive and low-frequency base disturbances, demonstrating the potential of TFCA for applications such as ambulance stretchers and human-sensitive transport systems. 2026-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60588 Title: Data-Driven Robust Subspace Predictive Control With Embedded Disturbance Observer Structure Author(s): Kong, Taejune; Dinkla, Rogier; Van Wingerden, Jan-Willem; Oomen, Tom; Oh, Sehoon Abstract: Subspace predictive control (SPC) is a data-driven control strategy that utilizes input-output measurements to predict future system behavior without requiring explicit model identification. Conventional SPC exhibits vulnerability to an unknown input disturbance, leading to degraded control performance and steady-state errors. To address these limitations, this article proposes a robust SPC method that inherently mitigates the effect of a constant input disturbance by augmenting the state-space representation through the internal model principle (IMP). This augmentation enables the controller to achieve integral action without requiring a separate disturbance observer (DOB) design. The proposed method is implemented in a data-driven framework, where an auxiliary disturbance is introduced into the data-driven algorithm to enhance disturbance rejection. A transfer function analysis verifies that the proposed Robust SPC eliminates a constant disturbance while maintaining the role of a DOB. Experimental validation on a two-inertia system confirms that the proposed method significantly improves reference tracking performance compared to conventional SPC, demonstrating its effectiveness in disturbance rejection without additional modeling complexity. 2026-06-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60585 Title: Robust Position Control of Series Elastic Actuator via Load Port Control and Novel Acceleration Estimation Author(s): Lee, Hyunwook; Cheon, Dasol; Oh, Sehoon Abstract: Precise position control of Series Elastic Actuator (SEA) is crucial yet challenging. Conventional methods like Load Feedback Control (LFC) compromise stability due to inherent phase lag, while Motor Feedback Control (MFC) can ensure stability but may introduce load-side vibrations during transients. This paper presents Load Port Control (LPC) as an alternative approach to achieve both stability and high-performance position tracking for SEA. Implementing LPC requires accurate load acceleration estimation, which is complicated by the limitations of existing methods; differentiation with low-pass filtering introduces phase delay, and simplified dynamics models are sensitive to external torques. To overcome this, we propose a novel acceleration estimation method that fuses low-pass filtered position information with high-pass filtered dynamics model data. This approach mitigates phase lag while enhancing robustness against external torque. The effectiveness of the LPC strategy is evaluated through theoretical analysis and experiments on a two-mass testbed. Under the evaluated conditions, the proposed method showed improved stability, tracking performance, and disturbance robustness relative to the compared methods. 2026-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60377 Title: 유연관절 로봇의 제어시스템 Author(s): 이덕진; 오세훈