Search Results (4)

This paper presents a high-order model-based robust control strategy for a dual-motor road wheel actuating system in a steer-by-wire (SbW) architecture. The system consists of a belt-driven and a pinion-driven motor collaboratively actuating the road wheels through mechanically coupled dynamics. To accurately capture the interaction between actuators, structural compliance, and road disturbances, a four-degree-of-freedom (4DOF) lumped-parameter model is developed. Leveraging this high-order dynamic model, a composite control framework is proposed, integrating feedforward model inversion, pole-zero feedback compensation, and a disturbance observer (DOB) to ensure precise trajectory tracking and disturbance rejection. High-fidelity co-simulations in MATLAB/Simulink and Siemens Amesim validate the effectiveness of the proposed control under various steering scenarios, including step and sine-sweep inputs. Compared to conventional low-order control methods, the proposed approach significantly reduces tracking error and demonstrates enhanced robustness and disturbance attenuation. These results highlight the critical role of high-order modeling in the precision control of dual-motor SbW systems and suggest its applicability in real-time, safety-critical vehicle steering applications. Full article

With frequent interactions between social media platforms, the dissemination of information and the interaction of opinions on the internet have become increasingly complex and diverse. This increase in information complexity not only affects the formation of public opinion but may also exacerbate the spread of diseases. Based on multilayer complex networks and combined with the Deffuant-I model, this paper explores the dual impact of information complexity and individual characteristics on both information and disease propagation. Through systematic simulation experiments, this paper analyzes the mechanisms of information complexity, individual compromise, and cognitive ability in the evolution of propagation. This study shows that the interactive effects of individual characteristics and information complexity have a significant impact on disease spread. This research not only provides a new theoretical perspective for understanding complex information dissemination but also offers valuable insights for public policymakers in promoting social harmony and addressing public health emergencies. Full article

Obtaining powered linear movement over a long range of motion is a common yet challenging task, as the majority of linear actuators have limited ranges of motion as determined by their functioning mechanisms. In this paper, the authors present a novel belt-based self-actuated linear drive (B-SALD), in which a self-powered moving platform slides on a slotted track with essentially unlimited range of motion (only limited by the length of the track). Unlike the traditional rack-and-pinion mechanism, the B-SALD system uses a double-sided timing belt as the power-transmitting element. With the teeth on its inner surface, the belt interacts with a timing pulley for its own circulation; with the teeth on its outer surface, the belt interacts with a linear rail with parallel slots and drives the translation of the moving platform. The unique functioning mechanism generates multiple distinct advantages: no lubrication is required; the slotted track is simple and inexpensive to manufacture; and it provides an inherent compliance to buffer shock loading. With the experiments conducted on a preliminary prototype, it has been demonstrated that the B-SALD is able to provide accurate positioning and continuous motion control, an overall mechanical efficiency of 70% over the majority of the load range, and the capability of generating large force output in the desired manner. Full article

Surface wear, as a major failure mode of gear systems, is an unavoidable phenomenon during the whole life of gears. It also induces other gear damages, such as fatigue cracks, surface pitting and spalling. Ultimately, those defects may result in the sudden failure of a gearbox transmission system, which can lead to a serious accident and unexpected economic loss. Therefore, it can provide huge cost and safety benefits to industries to monitor gear wear and predict its propagation. Gear wear raises the error rate of gear transmission systems, typically leading to improvements in dynamic loads, vibration, and noise. In return, the increased load conversely aggravates wear, creating a feedback cycle between dynamic responses and surface wear. For this purpose, a wear prediction model was incorporated into a tribo-dynamic model for quantitatively investigating how surface wear and gear vibration are mutually affected by each other. To obtain more precise dynamic responses, the tribo-dynamic model integrates the time-varying mesh stiffness, load-sharing ratio and friction parameters. To improve the computational efficiency and guarantee the calculation precision, an improved and updated wear depth methodology is constructed in the wear prediction model. This paper demonstrates the capability of the proposed dynamic wear prediction model in the investigation of the interaction effects between gear dynamics and surface wear, allowing for the development of improved gear wear prediction tools. The obtained results indicate that the surface wear impacts the dynamic characteristics, even with slight wear. In the initial stage of wear, the friction coefficient decreases slightly, largely due to the reduction in surface roughness; but the friction force increases because of the improved dynamic meshing force. Although the initial wear depth distributions of a pinion under dynamic and static conditions are similar, the wear depth distributions under dynamic conditions becomes significantly different compared to the those under static conditions with the wear process. The maximum wear depth of a pinion under dynamic conditions is about 1.6 times as the corresponding static conditions, when the wear cycle comes to 4 × 10⁴. Similarly, the maximum accumulative wear depth of a pinion under dynamic conditions reaches 1.2 times of that under static conditions. Therefore, the proposed dynamic wear prediction model is more appropriate to be applied to the surface wear of gears. Full article