Articles
Mechanics Based Design of Structures and Machines (15397742)53(8)pp. 5762-5794
This paper discusses vibrational energy harvesting, a technology that captures energy from ambient sources and transduces it into electric energy. The primary challenge in designing Vibration energy harvesters is that both power output and frequency bandwidth may not be simultaneously enhanced. Numerous studies have ventured into methodologies to augment power output and broaden the bandwidth for effective energy harvesting. However, these often hinge on beam structures, leading to intricate geometries that require substantial space. The research highlighted in this paper suggests innovative ideas based on multimodal energy harvesting techniques, devoid of the complexities inherent in previous designs. A semi-analytical electroelastic model for piezoelectric multi-plate energy harvesters, grounded in the principles of classical plate theory is proposed in this research. The mode shapes of the plate are numerically obtained using the differential quadrature method (DQM) under the clamped-free-clamped-free (CFCF) boundary conditions. Subsequently, modal analysis is employed to extract the output voltage of the system. The results are validated against pioneering research and various case studies are examined to explore the effect of diverse parameters on the magnitude and bandwidth of output voltage. The findings demonstrate that by fine-tuning the material properties of the harvester’s components, an optimal design can be realized. © 2025 Taylor & Francis Group, LLC.
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.
