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Deep Learning and Adaptive Control, 2nd Edition
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Deep Learning and Adaptive Control, 2nd Edition

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 5237

Special Issue 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
Institute of Artificial Intelligence & School of Automation and Electrical Engineering, 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 over-fitting. As the models become more and more complex, their interpretability will be reduced, and the performance and 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.

The Special Issue on the research progress of deep learning will help to 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 and 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 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.

Dr. Zhijia Zhao
Dr. Zhijie Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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

Published Papers (6 papers)

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Research

18 pages, 5643 KiB  
Article
Time Series Prediction Based on Multi-Scale Feature Extraction
by Ruixue Zhang and Yongtao Hao
Mathematics 2024, 12(7), 973; https://doi.org/10.3390/math12070973 - 25 Mar 2024
Viewed by 756
Abstract
Time series data are prevalent in the real world, particularly playing a crucial role in key domains such as meteorology, electricity, and finance. Comprising observations at historical time points, these data, when subjected to in-depth analysis and modeling, enable researchers to predict future [...] Read more.
Time series data are prevalent in the real world, particularly playing a crucial role in key domains such as meteorology, electricity, and finance. Comprising observations at historical time points, these data, when subjected to in-depth analysis and modeling, enable researchers to predict future trends and patterns, providing support for decision making. In current research, especially in the analysis of long time series, effectively extracting and integrating long-term dependencies with short-term features remains a significant challenge. Long-term dependencies refer to the correlation between data points spaced far apart in a time series, while short-term features focus on more recent changes. Understanding and combining these two features correctly are crucial for constructing accurate and reliable predictive models. To efficiently extract and integrate long-term dependencies and short-term features in long time series, this paper proposes a pyramid attention structure model based on multi-scale feature extraction, referred to as the MSFformer model. Initially, a coarser-scale construction module is designed to obtain coarse-grained information. A pyramid data structure is constructed through feature convolution, with the bottom layer representing the original data and each subsequent layer containing feature information extracted across different time step lengths. As a result, nodes higher up in the pyramid integrate information from more time points, such as every Monday or the beginning of each month, while nodes lower down retain their individual information. Additionally, a Skip-PAM is introduced, where a node only calculates attention with its neighboring nodes, parent node, and child nodes, effectively reducing the model’s time complexity to some extent. Notably, the child nodes refer to nodes selected from the next layer by skipping specific time steps. In this study, we not only propose an innovative time series prediction model but also validate the effectiveness of these methods through a series of comprehensive experiments. To comprehensively evaluate the performance of the designed model, we conducted comparative experiments with baseline models, ablation experiments, and hyperparameter studies. The experimental results demonstrate that the MSFformer model improves by 35.87% and 42.6% on the MAE and MSE indicators, respectively, compared to traditional Transformer models. These results highlight the outstanding performance of our proposed deep learning model in handling complex time series data, particularly in capturing long-term dependencies and integrating short-term features. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 2nd Edition)
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24 pages, 887 KiB  
Article
Searching by Topological Complexity: Lightweight Neural Architecture Search for Coal and Gangue Classification
by Wenbo Zhu, Yongcong Hu, Zhengjun Zhu, Wei-Chang Yeh, Haibing Li, Zhongbo Zhang and Weijie Fu
Mathematics 2024, 12(5), 759; https://doi.org/10.3390/math12050759 - 4 Mar 2024
Viewed by 747
Abstract
Lightweight and adaptive adjustment are key research directions for deep neural networks (DNNs). In coal industry mining, frequent changes in raw coal sources and production batches can cause uneven distribution of appearance features, leading to concept drift problems. The network architecture and parameters [...] Read more.
Lightweight and adaptive adjustment are key research directions for deep neural networks (DNNs). In coal industry mining, frequent changes in raw coal sources and production batches can cause uneven distribution of appearance features, leading to concept drift problems. The network architecture and parameters should be adjusted frequently to avoid a decline in model accuracy. This poses a significant challenge for those without specialist expertise. Although the Neural Architecture Search (NAS) has a strong ability to automatically generate networks, enabling the automatic design of highly accurate networks, it often comes with complex internal topological connections. These redundant architectures do not always effectively improve network performance, especially in resource-constrained environments, where their computational efficiency is significantly reduced. In this paper, we propose a method called Topology Complexity Neural Architecture Search (TCNAS). TCNAS proposes a new method for evaluating the topological complexity of neural networks and uses both topological complexity and accuracy to guide the search, effectively obtaining lightweight and efficient networks. TCNAS employs an adaptive shrinking search space optimization method, which gradually eliminates poorly performing cells to reduce the search space, thereby improving search efficiency and solving the problem of space explosion. In the classification experiments of coal and gangue, the optimal network designed by TCNAS has an accuracy of 83.3%. And its structure is much simpler, which is about 1/53 of the parameters of the network dedicated to coal and gangue recognition. Experiments have shown that TCNAS is able to generate networks that are both efficient and simple for resource-constrained industrial applications. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 2nd Edition)
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14 pages, 5063 KiB  
Article
Research on Deep Q-Network Hybridization with Extended Kalman Filter in Maneuvering Decision of Unmanned Combat Aerial Vehicles
by Juntao Ruan, Yi Qin, Fei Wang, Jianjun Huang, Fujie Wang, Fang Guo and Yaohua Hu
Mathematics 2024, 12(2), 261; https://doi.org/10.3390/math12020261 - 12 Jan 2024
Viewed by 675
Abstract
To adapt to the development trend of intelligent air combat, it is necessary to research the autonomous generation of maneuvering decisions for unmanned combat aerial vehicles (UCAV). This paper presents a maneuver decision-making method for UCAV based on a hybridization of deep Q-network [...] Read more.
To adapt to the development trend of intelligent air combat, it is necessary to research the autonomous generation of maneuvering decisions for unmanned combat aerial vehicles (UCAV). This paper presents a maneuver decision-making method for UCAV based on a hybridization of deep Q-network (DQN) and extended Kalman filtering (EKF). Firstly, a three-dimensional air combat simulation environment is constructed, and a flight motion model of UCAV is designed to meet the requirements of the simulation environment. Secondly, we evaluate the current situation of UCAV based on their state variables in air combat, for further network learning and training to obtain the optimal maneuver strategy. Finally, based on the DQN, the system state equation is constructed using the uncertain parameter values of the current network, and the observation equation of the system is constructed using the parameters of the target network. The optimal parameter estimation value of the DQN is obtained by iteratively updating the solution through EKF. Simulation experiments have shown that this autonomous maneuver decision-making method hybridizing DQN with EKF is effective and reliable, as it can eliminate the opponent and preserve its side. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 2nd Edition)
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16 pages, 19586 KiB  
Article
RFCT: Multimodal Sensing Enhances Grasping State Detection for Weak-Stiffness Targets
by Wenjun Ruan, Wenbo Zhu, Zhijia Zhao, Kai Wang, Qinghua Lu, Lufeng Luo and Wei-Chang Yeh
Mathematics 2023, 11(18), 3969; https://doi.org/10.3390/math11183969 - 19 Sep 2023
Viewed by 870
Abstract
Accurate grasping state detection is critical to the dexterous operation of robots. Robots must use multiple modalities to perceive external information, similar to humans. The direct fusion method of visual and tactile sensing may not provide effective visual–tactile features for the grasping state [...] Read more.
Accurate grasping state detection is critical to the dexterous operation of robots. Robots must use multiple modalities to perceive external information, similar to humans. The direct fusion method of visual and tactile sensing may not provide effective visual–tactile features for the grasping state detection network of the target. To address this issue, we present a novel visual–tactile fusion model (i.e., RFCT) and provide an incremental dimensional tensor product method for detecting grasping states of weak-stiffness targets. We investigate whether convolutional block attention mechanisms (CBAM) can enhance feature representations by selectively attending to salient visual and tactile cues while suppressing less important information and eliminating redundant information for the initial fusion. We conducted 2250 grasping experiments using 15 weak-stiffness targets. We used 12 targets for training and three for testing. When evaluated on untrained targets, our RFCT model achieved a precision of 82.89%, a recall rate of 82.07%, and an F1 score of 81.65%. We compared RFCT model performance with various combinations of Resnet50 + LSTM and C3D models commonly used in grasping state detection. The experimental results show that our RFCT model significantly outperforms these models. Our proposed method provides accurate grasping state detection and has the potential to provide robust support for robot grasping operations in real-world applications. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 2nd Edition)
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12 pages, 1089 KiB  
Article
Vibration Suppression of an Input-Constrained Wind Turbine Blade System
by Liang Cao and Shuangyin Liu
Mathematics 2023, 11(18), 3946; https://doi.org/10.3390/math11183946 - 17 Sep 2023
Cited by 1 | Viewed by 593
Abstract
During the actual wind power generation process, wind turbines are often affected by side effects such as blade vibrations, input constraints, and actuator faults. This can lead to a reduction in power generation efficiency and even result in unforeseen losses. This study discusses [...] Read more.
During the actual wind power generation process, wind turbines are often affected by side effects such as blade vibrations, input constraints, and actuator faults. This can lead to a reduction in power generation efficiency and even result in unforeseen losses. This study discusses a robust adaptive fault-tolerant boundary control approach to address the issues of input-constrained and actuator-fault problems in wind turbine blade vibration control. By employing projection mapping techniques and hyperbolic tangent functions, a novel robust adaptive controller based on online dynamic updates is constructed to constrain vibrations, compensate for unknown disturbance upper bounds, and ensure the robustness of the coupled system. Additionally, considering the possibility of actuator faults during the control process, a fault-tolerant controller is proposed to effectively suppress elastic vibrations in the wind turbine blade system even in the presence of actuator faults. The effectiveness of the proposed controller is validated through numerical simulations. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 2nd Edition)
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16 pages, 2127 KiB  
Article
Adaptive Control for an Aircraft Wing System with Hysteresis Nonlinearity
by Yi Qin, Fang Guo, Fujie Wang, Xing Li and Yaohua Hu
Mathematics 2023, 11(18), 3841; https://doi.org/10.3390/math11183841 - 7 Sep 2023
Cited by 1 | Viewed by 599
Abstract
This paper involves a novel adaptive control approach of a flexible wing system with hysteresis nonlinearity. The usual control design strategies based on the ordinary differential equations (ODEs) are inapplicable due to the flexible wing system described in the partial differential equations (PDEs), [...] Read more.
This paper involves a novel adaptive control approach of a flexible wing system with hysteresis nonlinearity. The usual control design strategies based on the ordinary differential equations (ODEs) are inapplicable due to the flexible wing system described in the partial differential equations (PDEs), and the design of the control algorithm becomes highly intricate. Firstly, the inverse dynamic model of hysteresis is introduced to compensate for the hysteresis nonlinearity. Considering the unknown external disturbances, an adaptive technique is utilized for compensation. Then, the direct Lyapunov approach is employed to prove the bounded stability of the system. Lastly, the effectiveness of the proposed approach is validated via simulation results. Full article
(This article belongs to the Special Issue Deep Learning and Adaptive Control, 2nd Edition)
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