A robust optimization approach for a MEMS accelerometer to minimize the effects of temperature variations is presented. The mathematical model of the accelerometer is built. The effects of temperature variations on the output performance of the accelerometer are determined, and thermal deformation of the accelerometer is analyzed. The deviations of the output capacitance and resonance frequency due to temperature fluctuations are calculated and discussed. The sensitivity analysis method is employed to determine the design variables for robust optimization and find out the key structural parameters that have most significant influence on the output capacitance and resonance frequency of the accelerometer. The mathematical model and procedure for the robust optimization of the accelerometer are proposed. The robust optimization problem is solved and discussed. The robust optimization results show that an optimized accelerometer with high sensitivity, high temperature robustness and decoupling structure is finally obtained.

Machined surface quality determines the reliability, wear resistance and service life of carbon fiber reinforced plastic (CFRP) workpieces. In this work, the formation mechanism of the surface topography and the machining defects of CFRPs are proposed, and the influence of milling parameters and fiber cutting angles on the surface quality of CFRPs is obtained, which can provide a reference for extended tool life and good surface quality. Trimming and slot milling tests of unidirectional CFRP laminates are performed. The surface roughness of the machined surface is measured, and the influence of milling parameters on the surface roughness is analyzed. A regression model for the surface roughness of CFRP milling is established. A significance test of the regression model is conducted. The machined surface topography of milling CFRP unidirectional laminates with different fiber orientations is analyzed, and the effect of fiber cutting angle on the surface topography of the machined surface is presented by using a digital super depth-of-field microscope and scanning electron microscope (SEM). To study the influence of fiber cutting angle on machining defects, the machined topography under different fiber orientations is analyzed. The slot milling defects and their formation mechanism under different fiber cutting angles are investigated.

Unmanned road roller has become an attractive solution to reduce operators’ working intensity and improve compaction efficiency. As a result of working in bumpy environments, sideslip effect is unavoidable during the compacting process. In order to guarantee the reliability and stability of path tracking control, a novel and effective method based on preview control and sideslip compensation is proposed in this paper. The kinematic and dynamic models of articulated road roller considering sideslip angles are obtained first. Then, a preview controller with sideslip compensation (PCSC) is developed to eliminate the path tracking errors between the actual path and the desired path. To evaluate the control performance of the proposed method, simulation under the MATLAB/Simulink and automatic compaction experiments at the construction site were performed with initial errors. Finally, the simulation and experimental results show that the path tracking errors can converge to zero as time goes by. The proposed method has the ability to navigate an articulated road roller to track a desired path in the existence of sideslip, only regarding the steering angle dynamic as control input. Compared with other benchmark method, it not only improves the control accuracy, but also increases the convergence rate, proving its effectiveness and feasibility. All these also demonstrate the practical value and validity of the proposed models with sideslip.

Lithium batteries are increasingly favored for energy storage due to their high energy density, long cycle life, and robust charge and discharge rates. However, safety concerns necessitate the implementation of a battery management system (BMS) to monitor battery status, maintain energy balance, and provide failure warnings to ensure safe operation. This paper proposes an efficient BMS for high‐voltage, high‐current lithium battery energy storage. The approach leverages a multihead‐attention‐enhanced long short‐term memory (LSTM) neural network combined with an adaptive unscented Kalman filter to accurately calculate the battery's state of charge (SOC) and state of health (SOH). To improve accuracy, various factors such as temperature and internal resistance were considered. The algorithm was validated through hardware and simulation experiments, with experimental data compared to estimation results to demonstrate its precision. The findings show strong convergence and tracking capabilities, with SOC estimation presenting a maximum error of 1.5% and SOH estimation a maximum error of under 0.4%. We expect that this approach will allow for a more refined evaluation of SOC and SOH in lithium‐ion batteries, potentially improving Li‐ion battery system management.

For the operation of Autonomous Mobile Robot (AMR) in unknown environments, accurate estimation of internal parameters and consequently precise prediction of the battery state of charge (SoC) are critical issues for power management. Battery performance can be affected by factors such as temperature deviation, discharge/charge current, Coulombic efficiency losses, and aging. Thus, in order to increase the model accuracy, it is important to update the model parameters online. In this paper, the Unscented Kalman Filter (UKF) is employed for the online estimation of the Lithium-Ion battery model parameters and the battery SoC based on the updated model. The proposed method is evaluated experimentally, and the results are compared with that of the Extended Kalman Filter (EKF). The comparison with the EKF shows that UKF provides better accuracy both in battery parameters estimation and the battery SoC estimation.