To improve the strong dependence of predictive control parameters on finite control set models, an improved robust finite control set predictive current control (FCS-MPCC) algorithm for permanent magnet synchronous motor (PMSM) drive systems is proposed. Firstly, the problems of complex calculation and inaccurate selection of voltage vectors in traditional FCS-MPCC are analyzed, and an extended voltage vector-based MPCC (EV ² -MPCC) based on the deadbeat principle is proposed to improve the steady-state performance of FCS-MPCC and also improve the anti-disturbance performance of the system. Secondly, the performance of PMSM under parameter mismatch is analyzed, and a sliding mode observer-based stator current and disturbance observer is proposed. The observed disturbance term is used to compensate the reference voltage term for quickly selecting the optimal voltage vector, and the estimated current replaces the traditional predicted current. This method further improves the robustness of EV ² -MPCC. Finally, the experimental results prove the effectiveness of the scheme.

In this article, an optimized-sector-based model predictive torque control (OSB-MPTC) strategy is proposed to reduce the torque ripple of the switched reluctance motors (SRMs). First, a phase torque estimation method based on the magnetic coenergy and Fourier series expansion is introduced. A new sector division rule and a selection of the basic voltage vector in predicting torque are then introduced. The cost function for minimizing torque error is designed to select the optimal voltage vector. In addition, a new reaching law is proposed to improve the robustness of the control system and the dynamic response of the speed loop. Finally, experiments are carried out on a 12/8 pole three-phase SRM prototype to verify the performance of the proposed control method in detail. Compared with the conventional model predictive control, the proposed OSB-MPTC with global robust sliding mode control has better performance in terms of suppression of the torque ripple, speed dynamic response, and robustness.

This article presents a nonlinear modeling method for the flux linkage and torque of a switched reluctance motor. The method is based on universal weighted least squares support vector machine regression (WLSSVR), combined with entropy method (EM) and improved coyote optimization algorithm (COA) to optimize the kernel parameters. Among them, EM can well improve the error of the general WLSSVR in the selection of sample weights, and the improved COA can prevent the system from falling into local optimum and improve the iteration speed. This SRM modeling method, which combines WLSSVR based on entropy and improved COA, can improve the modeling speed on the premise of ensuring the modeling accuracy mode speed. Finally, the effectiveness of the algorithm is verified by experiments.

Swift, non-destructive detection approaches should address the problem of insufficient sensitivity when attempting to obtain and perceive live crab information in low-light environments caused by the crab’s phototaxis. We propose a learning-based low-illumination image enhancer (LigED) for effective enhanced lighting and elimination of darkness in images. The camera response function was combined with the reflectance ground-truth mechanism of image decomposition. Self-attention units were then introduced in the reflectance restoration network to adjust the illumination to avoid visual defects, thus jointly strengthening the adaptability of dark-light enhancement and ability to perceive crab information. Convolutional neural network (CNN)-based detection methods can further enhance the algorithm’s robustness to light and adaptability to different environments, which motivated the development of a scalable lightweight live crab detector (EfficientNet-Det0) utilizing the two-stage compound scaling CNN approach. The lightness order error and natural image quality evaluator based on the proposed methods were 251.26 and 11.60, respectively. The quality of average precision detection increased by 13.84–95.40%. The fastest detection speed of a single image was 91.74/28.41 f·s⁻¹ using a common GPU/CPU, requiring only 15.1 MB of storage, which advocates for the utilization of LigED and EfficientNet-Det0 for the efficient detection of underwater live crabs.

Traditional track-based inspection schemes for caged poultry houses face issues with vulnerable tracks and cumbersome maintenance, while existing rail-less alternatives lack robust, reliable path planners. This study proposes TSO-HA*-Net, a hybrid global path planner that combines TSO-HA* with topological planning, which allows the inspection vehicle to continuously traverse a predetermined trackless route within each poultry house and conduct house-to-house inspections. Initially, the spatiotemporally optimized Hybrid A* (TSO-HA*) is employed as the lower-level planner to efficiently construct a semi-structured topological network by integrating predefined inspection rules into the global grid map of the poultry houses. Subsequently, the Dijkstra’s algorithm is adopted to plan a smooth inspection route that aligns with the starting and ending poses, conforming to the network. TSO-HA* retains the smoothness of HA* paths while reducing both time and computational overhead, thereby enhancing speed and efficiency in network generation. Experimental results show that compared to LDP-MAP and A*-dis, utilizing the distance reference tree (DRT) for h₂ calculation, the total planning time of the TSO-HA* algorithm is reduced by 66.6% and 96.4%, respectively, and the stored nodes are reduced by 99.7% and 97.4%, respectively. The application of the collision template in TSO-HA* results in a minimum reduction of 4.0% in front-end planning time, and the prior collision detection further decreases planning time by an average of 19.1%. The TSO-HA*-Net algorithm achieves global topological planning in a mere 546.6 ms, thereby addressing the critical deficiency of a viable global planner for inspection vehicles in poultry houses. This study provides valuable case studies and algorithmic insights for similar inspection task.

Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km² nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution of crabs in the pond. However, this method is inefficient and lacks automation. To address the problem of efficient and rapid detection of crabs via automated systems based on machine vision in low-brightness underwater environments, a two-step color correction and improved dark channel prior underwater image processing approach for crab detection is proposed in this paper. Firstly, the parameters of the dark channel prior are optimized with guided filtering and quadtrees to solve the problems of blurred underwater images and artificial lighting. Then, the gray world assumption, the perfect reflection assumption, and a strong channel to compensate for the weak channel are applied to improve the pixels of red and blue channels, correct the color of the defogged image, optimize the visual effect of the image, and enrich the image information. Finally, ShuffleNetV2 is applied to optimize the target detection model to improve the model detection speed and real-time performance. The experimental results show that the proposed method has a detection rate of 90.78% and an average confidence level of 0.75. Compared with the improved YOLOv5s detection results of the original image, the detection rate of the proposed method is increased by 21.41%, and the average confidence level is increased by 47.06%, which meets a good standard. This approach could effectively build an underwater crab distribution map and provide scientific guidance for crab farming.