Articles
Publication Date: 2026
Chaos, Solitons and Fractals (09600779)203
This study introduces a novel vibration suppression system by combining a Tuned Mass Damper with Inerter (TMDI) and a Vibro-Impact Nonlinear Energy Sink (VI-NES) in a parallel configuration. This configuration enhances the efficiency of the absorbers compared to a series arrangement, which tends to diminish energy reduction efficiency. The dynamic characteristics of this combined system are examined through intricate differential equations that model the motion of both the primary structure and its attached absorbers. Utilizing the method of multiple scales, the study investigates the system's responses to various conditions, including external forces and impacts. A chaotic, strongly modulated response is analyzed both analytically and numerically. The linear oscillator is subjected to an external force, and its dynamic behavior is examined using bifurcation analysis. This analysis aids in identifying the regions where a strongly modulated chaotic response occurs. Findings demonstrate that the integration of TMDI and VI-NES achieves superior vibration suppression compared to each device used independently. The research also delves into how parameters such as restitution coefficient, gap size, inertance, stiffness, and damping coefficient influence the vibrational behavior and energy dissipation efficiency of the system. Additionally, optimal settings for these parameters are identified to maximize the absorbers' performance. The optimal absorber is examined under harmonic forcing using bifurcation analysis. This innovative approach provides a practical and effective solution for vibration control in nonlinear systems using passive devices. © 2025 Elsevier Ltd.
Publication Date: 2025
International Journal of Systems Science (00207721)
This research proposes a novel hybrid force/position control (RL-HC) approach for robotic systems, utilising reinforcement learning. The proposed controller demonstrates superior robustness and performance, particularly in tracking the end-effector's position and contact force, compared to the hybrid sliding mode force/position control (HSMC) method, even in the presence of structural and non-structural uncertainties. The RL-HC controller is designed around a fixed-time sliding mode model, integrating force and position control components. The stability of this controller is established using the Lyapunov theory. Additionally, the reward function within the reinforcement learning network is carefully crafted to align with key objectives, including minimising chattering, force error, position error, and control effort. A simulation performed using a 3-DOF Delta Robot illustrates the effectiveness of the RL-HC approach. Results indicate that RL-HC outperforms traditional methods, showcasing better performance and robustness when facing various external disturbances and uncertainties. Specifically, the findings highlight a significant reduction in position error, force error, total control effort, and chattering. The study also illustrates how different reward function designs impact the achievement of the desired objectives. © 2025 Informa UK Limited, trading as Taylor & Francis Group.
