Based on analyzing the driving system of motors in series, it indicates that the system simplifies the structure and realizes the effective self-action differential when the electric vehicle(EV) runs on the level road without any variety or at low velocity. When the EV runs on the variational road, especially the friction force is variant, the EV is unstable according to the character of the permanent-magnet brushless DC motors (PMBDCMs). In this paper, the structure was improved and a μ-synthesis robust controller of a two-wheel steering vehicle is designed with the optimized weighting functions to attenuate the external disturbances while the yaw rate is chosen as the only feedback signal. The experimental result shows that the series connected two-wheel driving EV via μ-synthesis robust controller has better maneuverability and stronger ability to resist disturbances; it is not sensitive to the variations in vehicle parameters. The propose system improve the EV's maneuverability and stabilization.
Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.
A novel fractional PIalpha control strategy for pneumatic position servo system is presented in order to solve its strong non-linearity and low natural frequency problem. To demonstrate their better control characteristics, the fractional order control experiments under various conditions, which include step position signal, sine position signal with different frequency and amplitude, variety load etc, are carried out along with a detailed comparative analysis with traditional control. The results show the effectiveness of the proposed scheme and verify their fine control performance for pneumatic position servo system with the nonlinearity and parameter uncertainty.
CAN Communication System for electric vehicle was designed with MCU AT89C52,stand-alone CAN controller SJA1000 and PDIUSBD12 as the core elements.The hardware mainly completed the design of information collecting circuit and CAN-USB adapter.Many methods,such as photocoupler isolation,hardware filter,slope control mode were adoped to ensure the stability of the hardware system.Based on the hardware platform,the following softwares were designed,including the CAN node communication,the Firmware of the CAN-USB adapter,the PC host driver program and the application program,then the communication of the CAN node could be realized.Finallly,the electric vehicle was running at the state of no-load and in the road to test the sysetem relability.
A variable reluctance energy harvester (VREH) based on electromagnetic induction is developed for generating electrical energy from low-speed rotary motion. The challenge of a VREH at low rotational speeds is not only the low output power, but also the torque ripple that the harvester generates. Cogging torque, the major contribution to this torque ripple, is an inherent characteristic of VREH and is caused by its geometric features. Cogging torque produces acoustic noise and mechanical vibration for a drive system into which the VREH is embedded. This issue is of particular importance at low speeds and with light loads. In this paper, we use an m-shaped VREH as an example to propose a three-phase design in order to reduce the cogging torque but maintain a high output power at low speeds of 5 rpm to 20 rpm. Three identical m-shaped pickup units in a proper arrangement generate high amounts of electrical energy in three phases, but result in a lower torque ripple. Ten prototypes based on the proposed design were fabricated and tested, and their performance were in good agreement with the simulation results. By using the three pickup units in an optimized arrangement, the VREH enhances the energy harvesting performance in comparison to three single pickup units. At the same time, the torque ripple is reduced to one fifth of that produced by a single pickup unit. This demonstrates the strong potential of the three-phase VREH for implementations of self-powered wireless sensing systems in terms of energy output and mechanical effects on the rotary host.
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