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Research on the motion control of overhead cranes, constrained by underactuated characteristics, helps improve the efficiency of payload transportation. Most studies require all system state variables (trolley displacement, payload swing angle, and their velocities). In practice, sensors measure and transmit these variables, but noise affects their accuracy, reducing control performance. Additionally, uncertainties in crane parameters, unmodeled friction, and unknown disturbances threaten the system's stability. Traditional methods struggle to address these issues effectively. To address these challenges, this article proposes an adaptive discrete sliding mode control (DSMC) method with a Kalman filter. By extending the state system and considering disturbances as new variables, the Kalman filter effectively eliminates signal noise, accurately estimates disturbances, and estimates system states simultaneously. The proposed method incorporates disturbance compensators into the adaptive DSMC, utilizing exponential terms to suppress oscillations caused by excessively high or low control gains, thus increasing control speed. Experimental comparisons demonstrate the superiority and robustness of the proposed control method under various disturbance conditions. [ABSTRACT FROM AUTHOR]

Investigation of rarely studied gravel layers found in the loess in Shandong Province, eastern China, reveals the fabric characteristics of two gravel layers (G1, G2) and the sedimentary characteristics of loess at the typical and well‐preserved Heiyu section (HY), where, to determine the paleoclimatic changes during Marine Isotope Stage 3a. Optically stimulated luminescence dates of the HY formation range from 0.26 ± 0.02 ka to 39.00 ± 2.00 ka. In addition, the ages of G1 and G2 were estimated using the Bayesian model to be 39.60–40.50 and 29.00–29.50 ka. G1 and G2 are mainly composed of fine and medium gravel, both of which were subangular to subrounded limestone, with gravel directions to NE and E. The average flow velocity, average depth, and flood peak flow of G1 are 1.10 m/s, 0.49 m, and 37.04 m3/s, respectively, calculated using the flow energy method, whereas those of G2 are 0.98 m/s, 0.38 m, and 18.38 m3/s, respectively. Analysis of climate proxy indices show that the sedimentary environment of the gravel and loess in HY might be a regional response to global change. [ABSTRACT FROM AUTHOR]

Summary: Due to the long‐term operation of the tower crane, various faults will inevitably occur, such as actuator faults, which further increases the difficulty of controller design. In this paper, a fault‐tolerant control method with adaptive gain is proposed for the double‐pendulum tower crane with distributed payload beam (DTCDPB) system in the existence of external disturbances and actuator failures. First, a nonlinear fault‐tolerant observer is designed for fault diagnosis. Second, an S‐shaped trajectory functions are coupled online and embedded into the system target trajectories for payload swing suppression. Then, feedforward compensation is applied in the controller for the observed faults. Moreover, the adaptive suppression term to be activated is also designed in the controller taking into account the errors existing in the observation. Finally, the effectiveness of the proposed observer and controller is experimentally verified. [ABSTRACT FROM AUTHOR]

Three-dimensional target detection is one of the key technologies for intelligent driving, but problems like large amount of processed data and many preset feature parameters still exist. To remedy the problem of poor correlation between 3D feature selection settings and actual target features, a detection method that incorporates point-piilars networks and DETR is proposed. Firstly, piilar coding is employed to offset the influence of redundant point clouds, which improves both computational efficiency and the match of feature extraction. Secondly, the prediction module based on the DETR decoder adopts a multi-headed attention mechanism to establish the correlation mapping between global features and the prediction set, and the prediction results with the strongest correlation are produced in parallel, avoiding the uncertainty caused by manual reliance on the intervention parameters of priori knowledge. Finally, the method is validated on the publicly available dataset, and the mAP is increased by 19. 14% and FPS by 3 compared with the original point-piilars network. Also, they both improve substantially when compared to other typical algorithms. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) are widely applied in industry, and proportional integral (PI) controllers are often used to control PMSMs. Aiming at the characteristics of the poor anti-disturbance ability and speed ripple of traditional PI controllers, a novel composite speed controller for PMSMs is proposed in this paper that uses a novel sliding mode control (SMC). To improve the chattering problem of traditional SMC, a high-order approaching law super-twisting algorithm (STA) is applied. Considering the internal and external disturbance of motor driver systems, such as motor parameter drifts and load torque changes, a disturbance estimator based on an extended state observer (ESO) is proposed, and it is used for the feed-forward compensation of the current. The composite super-twisting integral sliding mode controller (ST-ISMC) with a nonlinear ESO is tested by simulations and experiments, and the comparative results verify that the proposed controller has the higher control accuracy, smaller speed ripple and stronger robustness. [ABSTRACT FROM AUTHOR]