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Deep Learning and Adaptive Control, 4th Edition
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Deep Learning and Adaptive Control, 4th Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1968

Editors

Prof. Dr. Zhijia Zhao

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Interests: adaptive control; learning control; flexible mechanical systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhijie Liu

E-Mail Website
Guest Editor
School of Intelligent Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
Interests: boundary control of distributed parameter systems; soft robots; intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning is a research hotspot in artificial intelligence, machine learning, and data science. It has made many achievements in search technology, machine learning, machine translation, natural language processing, and other related fields. The applications of deep learning are undoubtedly worthy of attention. Recent results in deep learning have left no doubt that it is amongst the most powerful modeling and control tools that we possess. The real question is how we can utilize deep learning for control without losing stability and performance guarantees. At present, with the increasing amount of data to be processed, the calculation process is more complex and cumbersome than before, and the efficiency of the algorithm may be reduced due to overfitting. As the models become increasingly complex, their interpretability will be reduced, and the performance as well as efficacy of the algorithms will be reduced accordingly, which requires further research. Even though recent successes in deep reinforcement learning (DRL) have shown that deep learning can be a powerful value function approximator, several key questions must be answered before deep learning enables a new frontier in unmanned systems.

This Special Issue on the research progress in deep learning will help update the most advanced methods, technologies, and applications in this field. DRL is closely tied theoretically to adaptive control. Recent work has shown how to use DRL to develop new forms of adaptive controllers that effectively deal with some existing open problems in adaptive control, such as handling unmatched uncertainties. Any actual system has varying degrees of uncertainty. When facing the changes in internal characteristics and the influence of external disturbances, it is necessary to adopt adaptive control. Since its first development, adaptive control has been keeping pace with the development of science and engineering, and more new methods as well as applications have been introduced over time. This Special Issue aims to introduce the latest progress in adaptive control theory and application. The key points are system modeling, parameter identification, structural analysis, controller design, performance analysis, and the application research results of adaptive control algorithms. We are looking for the latest research results in deep learning and adaptive control. Topics of interest include, but are not limited to, the keywords listed below.

Prof. Dr. Zhijia Zhao
Prof. Dr. Zhijie Liu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • CNN
  • RNN
  • transformer model
  • optimization of deep learning
  • applications of deep learning
  • reinforcement-learning-based control
  • applications of reinforcement learning
  • adaptive iterative learning control
  • modeling of adaptive systems
  • design of adaptive controllers
  • application of adaptive control

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Related Special Issues

Published Papers (3 papers)

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14 pages, 16317 KB  
Article
Cross-Purification Mask Network: A Mask Refinement Method for Single-Channel Speech Separation
by Fuwen Zhu, Kaihao Yao and Keping Wang
Mathematics 2026, 14(10), 1709; https://doi.org/10.3390/math14101709 - 15 May 2026
Viewed by 199
Abstract
Accurate target speech mask estimation is the key to single-channel speech separation. Masks generated by conventional mask networks are easily corrupted by interfering speech and background noise, which degrades separation performance. To solve this problem, this paper proposes a Cross-Purification Mask Network (CPMN), [...] Read more.
Accurate target speech mask estimation is the key to single-channel speech separation. Masks generated by conventional mask networks are easily corrupted by interfering speech and background noise, which degrades separation performance. To solve this problem, this paper proposes a Cross-Purification Mask Network (CPMN), which consists of three core modules: the Dynamic Context-Aware Mechanism (DCAM), Feature Cross-Complementation Mechanism (FCCM), and Adaptive Purification Mask Mechanism (APMM). The DCAM aggregates dynamic sliding window and long-term temporal features to capture long-range temporal dependencies of masks and enhance the localization accuracy of target speech. The FCCM fuses weighted mask features of interfering speakers to dynamically supplement missing information in target speech masks. The APMM combines adaptive filters and residual networks to output high-precision refined masks. The CPMN is embedded into three mainstream speech separation frameworks including Conv-TasNet, DPTNet, and TDANet, and extensive experiments are conducted on Libri2Mix, WHAM!, and WSJ0-2Mix datasets. The results show that the CPMN brings stable performance gains. After integration, TDANet achieves SI-SNRi of 17.4 dB (+0.5 dB) on Libri2Mix and 15.2 dB (+0.4 dB) on WHAM!. Meanwhile, Conv-TasNet and DPTNet obtain SI-SNR improvements of 0.3 dB (15.6 dB) and 0.4 dB (20.8 dB) on WSJ0-2Mix, respectively. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 4th Edition)
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22 pages, 5599 KB  
Article
An Adaptive State-Space Convolutional Fusion Network for High-Precision Pest Detection in Smart Agarwood Cultivation
by Zhijie Luo, Rui Chen, Shaoxin Li and Jianjun Guo
Mathematics 2025, 13(24), 3937; https://doi.org/10.3390/math13243937 - 10 Dec 2025
Viewed by 545
Abstract
The sustainable cultivation of agarwood, a high-value tree species, is significantly threatened by foliar pests, requiring efficient and accurate monitoring solutions. While deep learning is widely used, mainstream models face inherent limitations: Convolutional Neural Networks have restricted receptive fields and Transformers incur high [...] Read more.
The sustainable cultivation of agarwood, a high-value tree species, is significantly threatened by foliar pests, requiring efficient and accurate monitoring solutions. While deep learning is widely used, mainstream models face inherent limitations: Convolutional Neural Networks have restricted receptive fields and Transformers incur high computational complexity, complicating the balance of accuracy and efficiency for tiny pest detection in complex environments. To address these challenges, a novel Adaptive State-space Convolutional Fusion Network (ASCNet) is proposed. Its core component, the Adaptive State-space Convolutional Fusion Block (ASBlock), integrates the global context modeling of state-space models—which have linear complexity—with the local feature extraction of convolutional networks through a dual-path adaptive fusion mechanism. A Grouped Spatial Shuffle Downsampling (GSD) module replaces standard strided convolutions to preserve fine-grained spatial details during downsampling. For small object detection, a Normalized Wasserstein Distance (NWD)-based loss function mitigates the sensitivity of traditional IoU to minor localization errors. Evaluations on a new agarwood pest dataset show that ASCNet outperforms state-of-the-art detectors (including the YOLO series, RT-DETR, and Gold-YOLO), achieving a maximum mAP@50 of 93.0 ± 0.2% and mAP@50:95 of 71.2 ± 0.3% with high computational efficiency. The results confirm ASCNet as a robust and effective solution for intelligent pest monitoring in high-value crops like agarwood. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 4th Edition)
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31 pages, 17746 KB  
Article
Improved YOLO11 for the Asian Citrus Psyllid on Yellow Sticky Traps: A Lightweight Design for Edge Deployment
by Liang Cao, Wei Xiao, Yexin Mo, Shaoxuan Zeng, Hua Chen, Zhongzhen Wu and Xiangli Li
Mathematics 2025, 13(23), 3836; https://doi.org/10.3390/math13233836 - 30 Nov 2025
Cited by 2 | Viewed by 697
Abstract
Citrus Huanglongbing (HLB) is one of the most destructive diseases in the global citrus industry; its pathogen is transmitted primarily by the Asian citrus psyllid (ACP), Diaphorina citri Kuwayama, making timely monitoring and control of ACP populations essential. Real-world ACP monitoring faces several [...] Read more.
Citrus Huanglongbing (HLB) is one of the most destructive diseases in the global citrus industry; its pathogen is transmitted primarily by the Asian citrus psyllid (ACP), Diaphorina citri Kuwayama, making timely monitoring and control of ACP populations essential. Real-world ACP monitoring faces several challenges, including tiny targets easily confused with the background, noise amplification and spurious detections caused by textures, stains, and specular glare on yellow-boards, unstable localization due to minute shifts of small boxes, and strict constraints on parameters, computation, and model size for long-term edge deployment. To address these challenges, we focus on the yellow-board ACP monitoring scenario and create the ACP Yellow Sticky Trap Dataset (ACP-YSTD), which standardizes background and acquisition procedures, covering common interference sources. The dataset consists of 600 images with 3837 annotated ACP, serving as a unified basis for training and evaluation. On the modeling side, we propose TGSP-YOLO11, an improved YOLO11-based detector: the detection head is reconfigured to the two scales P2 + P3 to match tiny targets and reduce redundant paths; Guided Scalar Fusion (GSF) is introduced on the high-resolution branch to perform constrained, lightweight scalar fusion that suppresses noise amplification; ShapeIoU is adopted for bounding-box regression to enhance shape characterization and alignment robustness for small objects; and Network Slimming is employed for channel-level structured pruning, markedly reducing parameters, FLOPs, and model size to satisfy edge deployment, without degrading detection performance. Experiments show that on the ACP-YSTD test set, TGSP-YOLO11 achieves precision 92.4%, recall 95.5%, and F1 93.9, with 392,591 parameters, a model size of 1.4 MB, and 6.0 GFLOPs; relative to YOLO11n, recall increases by 4.6%, F1 by 2.4, and precision by 0.2%, while the parameter count, model size, and computation decrease by 84.8%, 74.5%, and 4.8%, respectively. Compared to representative detectors (SSD, RT-DETR, YOLOv7-tiny, YOLOv8n, YOLOv9-tiny, YOLOv10n, YOLOv12n, YOLOv13n), TGSP-YOLO11 improves recall by 33.9%, 19.0%, 8.5%, 10.1%, 6.3%, 4.6%, 6.9%, and 5.7%, respectively, and F1 by 19.9, 14.9, 5.1, 6.0, 2.6, 5.6, 3.6, and 3.9, respectively. Additionally, it reduces parameter count, model size, and computation by 84.0–98.8%, 74.5–97.9%, and 3.2–94.2%, respectively. Transfer evaluation indicates that on 20 independent yellow-board images not seen during training, the model attains precision 94.3%, recall 95.8%, F1 95.0, and 159.2 FPS. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 4th Edition)
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