Similar papers
2026
Physics-Constrained Residual Learning for Refined Dynamic Identification and Robust Torque Estimation
Accurate dynamic modeling of industrial robots is essential for high-performance control and estimating torque. However, traditional physics-based models often fail to capture unmodeled dynamics such as complex friction, payload variations, and gearbox-induced distortions. This article refines a physics-based modeling...
Fauzy Satrio Wibowo, Hsien-I Lin, Wen-Hui Chen
· IEEE Transactions on Instrum... · 0 citations
Preprint
Aug 2026
Control-Oriented Learning for Dynamic Tracking and Stability Analysis of Soft Pneumatic Actuators
Soft pneumatic actuators offer inherent compliance and safe interaction but remain difficult to model and control because of their highly nonlinear, distributed dynamics. We present a control-oriented data-driven modeling and control framework that decomposes actuator behavior into a nonlinear static equilibrium model...
Nithin S. Kumar, Eric J. Barth
· 0 citations
Open access
Aug 2026
Reinforcement learning-based adaptive gyroscopic control for torsional response mitigation: a proof-of-concept study
Torsional vibrations induced by seismic excitation can significantly increase structural demand, particularly in systems with geometric or stiffness irregularities. This proof-of-concept study proposes a hybrid adaptive control framework that combines passive gyroscopic damping with reinforcement learning (RL)-based...
S. Stephen, Ali Hadi, O. Akinradewo et al.
· Asian Journal of Civil Engin... · 0 citations
Open access
Aug 2026
Feedback-Linearization-Assisted Observer-Based Interconnection and Damping Assignment Passivity Control for Electromechanical Actuators
Electromechanical actuators (EMAs) are increasingly used in aerospace servo actuation because of their compact structure, high power density, and convenient integration with electric flight-control systems. However, load-side aerodynamic torque, friction, parameter perturbations, and unmodeled transmission effects ente...
Xi Xiao, Xuming Cheng, Bo-Hao Li et al.
· Actuators · 0 citations
Open access
2026
Operational temperature effect on MR dampers in stay cable vibration control : an adaptive RL-based control strategy
This paper proposes a reinforcement-learning (RL)-based semi-active control strategy to mitigate stay-cable vibrations using magnetorheological (MR) dampers while explicitly addressing MR fluid temperature variations during damper operation. The controller is model-free and based on the Q-learning method. The controlle...
Y. M. Delijani, Shao-Hong Cheng, F. Ghrib
· International Symposium on D... · 0 citations
Conference
Jul 2026
Hybrid Computed Torque Control and Soft Actor-Critic Framework for Sample-Efficient Robotic Manipulator Control
Reinforcement Learning (RL) has emerged as a promising approach for robotic control, enabling agents to learn control policies through interaction with complex and dynamic environments. However, standalone RL methods often suffer from poor sample efficiency, limiting their practicality for real-world robotic systems. T...
Mona Alsbakhi, Mohammed M. Lubbad, M. Tabash et al.
· International Conference on... · 0 citations