A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

Vol. 9,  No. 3, 2022

Display Method:
Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey
Jun Zhang, Lei Pan, Qing-Long Han, Chao Chen, Sheng Wen, Yang Xiang
2022, 9(3): 377-391. doi: 10.1109/JAS.2021.1004261
Abstract(1636) HTML (531) PDF(306)
With the booming of cyber attacks and cyber criminals against cyber-physical systems (CPSs), detecting these attacks remains challenging. It might be the worst of times, but it might be the best of times because of opportunities brought by machine learning (ML), in particular deep learning (DL). In general, DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data. DL models are adopted quickly to cyber attacks against CPS systems. In this survey, a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context. A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems. The methodology includes CPS scenario analysis, cyber attack identification, ML problem formulation, DL model customization, data acquisition for training, and performance evaluation. The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules. Moreover, excellent performance is achieved partly because of several high-quality datasets that are readily available for public use. Furthermore, challenges, opportunities, and research trends are pointed out for future research.
Sliding Mode Control in Power Converters and Drives: A Review
Ligang Wu, Jianxing Liu, Sergio Vazquez, Sudip K. Mazumder
2022, 9(3): 392-406. doi: 10.1109/JAS.2021.1004380
Abstract(751) HTML (281) PDF(130)
Sliding mode control (SMC) has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched disturbances. The aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic systems. The fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives, such chattering phenomenon and variable switching frequency, are discussed and analyzed. The recent developments in SMC systems, future challenges and perspectives of SMC for power converters are discussed.
Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions
Mohamed Amine Ferrag, Lei Shu, Othmane Friha, Xing Yang
2022, 9(3): 407-436. doi: 10.1109/JAS.2021.1004344
Abstract(2575) HTML (568) PDF(190)
In this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber security. Specifically, we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0. Then, we evaluate intrusion detection systems according to emerging technologies, including, Cloud computing, Fog/Edge computing, Network virtualization, Autonomous tractors, Drones, Internet of Things, Industrial agriculture, and Smart Grids. Based on the machine learning technique used, we provide a comprehensive classification of intrusion detection systems in each emerging technology. Furthermore, we present public datasets, and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0. Finally, we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.
Barrier-Certified Learning-Enabled Safe Control Design for Systems Operating in Uncertain Environments
Zahra Marvi, Bahare Kiumarsi
2022, 9(3): 437-449. doi: 10.1109/JAS.2021.1004347
Abstract(648) HTML (315) PDF(77)
This paper presents learning-enabled barrier-certified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact. That is, safety constraints are imposed by not only the ego system’s own physical limitations but also other systems operating nearby. Since the model of the external agent is required to impose control barrier functions (CBFs) as safety constraints, a safety-aware loss function is defined and minimized to learn the uncertain and unknown behavior of external agents. More specifically, the loss function is defined based on barrier function error, instead of the system model error, and is minimized for both current samples as well as past samples stored in the memory to assure a fast and generalizable learning algorithm for approximating the safe set. The proposed model learning and CBF are then integrated together to form a learning-enabled zeroing CBF (L-ZCBF), which employs the approximated trajectory information of the external agents provided by the learned model but shrinks the safety boundary in case of an imminent safety violation using instantaneous sensory observations. It is shown that the proposed L-ZCBF assures the safety guarantees during learning and even in the face of inaccurate or simplified approximation of external agents, which is crucial in safety-critical applications in highly interactive environments. The efficacy of the proposed method is examined in a simulation of safe maneuver control of a vehicle in an urban area.
Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration
Lujuan Dang, Badong Chen, Yulong Huang, Yonggang Zhang, Haiquan Zhao
2022, 9(3): 450-465. doi: 10.1109/JAS.2021.1004350
Abstract(973) HTML (361) PDF(126)
Traditional cubature Kalman filter (CKF) is a preferable tool for the inertial navigation system (INS)/global positioning system (GPS) integration under Gaussian noises. The CKF, however, may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances. To address this issue, a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points (MEEF-CKF) is proposed. The MEEF-CKF behaves a strong robustness against complex non-Gaussian noises by operating several major steps, i.e., regression model construction, robust state estimation and free parameters optimization. More concretely, a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step. The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points (MEEF) under the framework of the regression model. In the MEEF-CKF, a novel optimization approach is provided for the purpose of determining free parameters adaptively. In addition, the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic. The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex non-Gaussian noises.
Conflict-Aware Safe Reinforcement Learning: A Meta-Cognitive Learning Framework
Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares
2022, 9(3): 466-481. doi: 10.1109/JAS.2021.1004353
Abstract(624) HTML (282) PDF(67)
In this paper, a data-driven conflict-aware safe reinforcement learning (CAS-RL) algorithm is presented for control of autonomous systems. Existing safe RL results with pre-defined performance functions and safe sets can only provide safety and performance guarantees for a single environment or circumstance. By contrast, the presented CAS-RL algorithm provides safety and performance guarantees across a variety of circumstances that the system might encounter. This is achieved by utilizing a bilevel learning control architecture: A higher meta-cognitive layer leverages a data-driven receding-horizon attentional controller (RHAC) to adapt relative attention to different system’s safety and performance requirements, and, a lower-layer RL controller designs control actuation signals for the system. The presented RHAC makes its meta decisions based on the reaction curve of the lower-layer RL controller using a meta-model or knowledge. More specifically, it leverages a prediction meta-model (PMM) which spans the space of all future meta trajectories using a given finite number of past meta trajectories. RHAC will adapt the system’s aspiration towards performance metrics (e.g., performance weights) as well as safety boundaries to resolve conflicts that arise as mission scenarios develop. This will guarantee safety and feasibility (i.e., performance boundness) of the lower-layer RL-based control solution. It is shown that the interplay between the RHAC and the lower-layer RL controller is a bilevel optimization problem for which the leader (RHAC) operates at a lower rate than the follower (RL-based controller) and its solution guarantees feasibility and safety of the control solution. The effectiveness of the proposed framework is verified through a simulation example.
Full-State-Constrained Non-Certainty-Equivalent Adaptive Control for Satellite Swarm Subject to Input Fault
Zhiwei Hao, Xiaokui Yue, Haowei Wen, Chuang Liu
2022, 9(3): 482-495. doi: 10.1109/JAS.2021.1004216
Abstract(895) HTML (451) PDF(99)
Satellite swarm coordinated flight (SSCF) technology has promising applications, but its complex nature poses significant challenges for control implementation. In response, this paper proposes an easily solvable adaptive control scheme to achieve high-performance trajectory tracking of the SSCF system subject to actuator efficiency losses and external disturbances. Most existing adaptive controllers based on the certainty-equivalent (CE) principle show unpredictability and non-convergence in their online parameter estimations. To overcome the above vulnerabilities and the difficulties caused by input failures of SSCF, this paper proposes an adaptive estimator based on scaling immersion and invariance (I&I), which reduces the computational complexity while improving the performance of the parameter estimator. Besides, a barrier Lyapunov function (BLF) is applied to satisfy both the boundedness of the system states and the singularity avoidance of the computation. It is proved that the estimator error becomes sufficiently small to converge to a specified attractive invariant manifold and the closed-loop SSCF system can obtain asymptotic stability under full-state constraints. Finally, numerical simulations are performed for comparison and analysis to verify the effectiveness and superiority of the proposed method.
Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things
Ruhul Amin Khalil, Nasir Saeed, Mohammad Inayatullah Babar, Tariqullah Jan, Sadia Din
2022, 9(3): 496-509. doi: 10.1109/JAS.2021.1004356
Abstract(698) HTML (353) PDF(35)
Localization of sensor nodes in the internet of underwater things (IoUT) is of considerable significance due to its various applications, such as navigation, data tagging, and detection of underwater objects. Therefore, in this paper, we propose a hybrid Bayesian multidimensional scaling (BMDS) based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical, magnetic induction, and acoustic technologies. These communication technologies are already used for communication in the underwater environment; however, lacking localization solutions. Optical and magnetic induction communication achieves higher data rates for short communication. On the contrary, acoustic waves provide a low data rate for long-range underwater communication. The proposed method collectively uses optical, magnetic induction, and acoustic communication-based ranging to estimate the underwater sensor nodes’ final locations. Moreover, we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound (H-CRLB). Simulation results provide a complete comparative analysis of the proposed method with the literature.
A Visual-Based Gesture Prediction Framework Applied in Social Robots
Bixiao Wu, Junpei Zhong, Chenguang Yang
2022, 9(3): 510-519. doi: 10.1109/JAS.2021.1004243
Abstract(851) HTML (393) PDF(72)
In daily life, people use their hands in various ways for most daily activities. There are many applications based on the position, direction, and joints of the hand, including gesture recognition, gesture prediction, robotics and so on. This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap Motion to predict dynamic hand gestures. The model is applied to the NAO robot to verify the effectiveness of the proposed method. First of all, in order to reduce jitter or jump generated in the process of data acquisition by the Leap Motion, the Kalman filter is applied to the original data. Then some new feature descriptors are introduced. The length feature, angle feature and angular velocity feature are extracted from the filtered data. These features are fed into the long-short time memory recurrent neural network (LSTM-RNN) with different combinations. Experimental results show that the combination of coordinate, length and angle features achieves the highest accuracy of 99.31%, and it can also run in real time. Finally, the trained model is applied to the NAO robot to play the finger-guessing game. Based on the predicted gesture, the NAO robot can respond in advance.
Optimal Synchronization Control of Heterogeneous Asymmetric Input-Constrained Unknown Nonlinear MASs via Reinforcement Learning
Lina Xia, Qing Li, Ruizhuo Song, Hamidreza Modares
2022, 9(3): 520-532. doi: 10.1109/JAS.2021.1004359
Abstract(776) HTML (281) PDF(110)
The asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multiagent systems (MASs) is considered in the paper. Intuitively, a state-space transformation is performed such that satisfaction of symmetric input constraints for the transformed system guarantees satisfaction of asymmetric input constraints for the original system. Then, considering that the leader’s information is not available to every follower, a novel distributed observer is designed to estimate the leader’s state using only exchange of information among neighboring followers. After that, a network of augmented systems is constructed by combining observers and followers dynamics. A nonquadratic cost function is then leveraged for each augmented system (agent) for which its optimization satisfies input constraints and its corresponding constrained Hamilton-Jacobi-Bellman (HJB) equation is solved in a data-based fashion. More specifically, a data-based off-policy reinforcement learning (RL) algorithm is presented to learn the solution to the constrained HJB equation without requiring the complete knowledge of the agents’ dynamics. Convergence of the improved RL algorithm to the solution to the constrained HJB equation is also demonstrated. Finally, the correctness and validity of the theoretical results are demonstrated by a simulation example.
A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis
Hao Wu, Xin Luo, MengChu Zhou, Muhyaddin J. Rawa, Khaled Sedraoui, Aiiad Albeshri
2022, 9(3): 533-546. doi: 10.1109/JAS.2021.1004308
Abstract(690) HTML (314) PDF(55)
A large-scale dynamically weighted directed network (DWDN) involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications, like in a terminal interaction pattern analysis system (TIPAS). It can be represented by a high-dimensional and incomplete (HDI) tensor whose entries are mostly unknown. Yet such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN. A latent factorization-of-tensors (LFT) model proves to be highly efficient in extracting such knowledge from an HDI tensor, which is commonly achieved via a stochastic gradient descent (SGD) solver. However, an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale DWDNs. To address this issue, this work proposes a proportional-integral-derivative (PID)-incorporated LFT model. It constructs an adjusted instance error based on the PID control principle, and then substitutes it into an SGD solver to improve the convergence rate. Empirical studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models, the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.
Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition
Kun Zhu, Chengpu Yu, Yiming Wan
2022, 9(3): 547-555. doi: 10.1109/JAS.2021.1004362
Abstract(913) HTML (460) PDF(78)
In this paper, a new recursive least squares (RLS) identification algorithm with variable-direction forgetting (VDF) is proposed for multi-output systems. The objective is to enhance parameter estimation performance under non-persistent excitation. The proposed algorithm performs oblique projection decomposition of the information matrix, such that forgetting is applied only to directions where new information is received. Theoretical proofs show that even without persistent excitation, the information matrix remains lower and upper bounded, and the estimation error variance converges to be within a finite bound. Moreover, detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition (VDF-ED). It is revealed that under non-persistent excitation, part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data, which could produce a more ill-conditioned information matrix than our proposed algorithm. Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.
Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL
Xiaofei Zhang, Hongbin Ma, Wenchao Zuo, Man Luo
2022, 9(3): 556-563. doi: 10.1109/JAS.2019.1911801
Abstract(1214) HTML (734) PDF(96)
Random vector functional ink (RVFL) networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected. Their network structure in which contains the direct links between inputs and outputs isunique, and stability analysis and real-time performance are two difficulties of the control systems based on neural networks. In this paper, combining the advantages of RVFL and the ideas of online sequential extreme learning machine (OS-ELM) and initial-training-free online extreme learning machine (ITF-OELM), a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm (ITF-ORVFL) is investigated for training RVFL. The link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed, and the stability for nonlinear systems based on this learning algorithm is analyzed. The experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.
QoS Prediction Model of Cloud Services Based on Deep Learning
WenJun Huang, PeiYun Zhang, YuTong Chen, MengChu Zhou, Yusuf Al-Turki, Abdullah Abusorrah
2022, 9(3): 564-566. doi: 10.1109/JAS.2021.1004392
Abstract(629) HTML (213) PDF(126)
Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning
Junjie Wang, Qichao Zhang, Dongbin Zhao
2022, 9(3): 567-569. doi: 10.1109/JAS.2021.1004395
Abstract(587) HTML (207) PDF(148)
Multi-Cluster Feature Selection Based on Isometric Mapping
Yadi Wang, Zefeng Zhang, Yinghao Lin
2022, 9(3): 570-572. doi: 10.1109/JAS.2021.1004398
Abstract(541) HTML (188) PDF(123)