Early Access

Display Method:
Adaptive Sensor-Fault Tolerant Control of Unmanned Underwater Vehicles With Input Saturation
Xuerao Wang, Qingling Wang, Yanxu Su, Yuncheng Ouyang, Changyin Sun
, Available online  
Abstract:
This paper investigates the tracking control problem for unmanned underwater vehicles (UUVs) systems with sensor faults, input saturation, and external disturbance caused by waves and ocean currents. An active sensor fault-tolerant control scheme is proposed. First, the developed method only requires the inertia matrix of the UUV, without other dynamic information, and can handle both additive and multiplicative sensor faults. Subsequently, an adaptive fault-tolerant controller is designed to achieve asymptotic tracking control of the UUV by employing robust integral of the sign of error feedback method. It is shown that the effect of sensor faults is online estimated and compensated by an adaptive estimator. With the proposed controller, the tracking error and estimation error can asymptotically converge to zero. Finally, simulation results are performed to demonstrate the effectiveness of the proposed method.
Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection
Fei Ming, Wenyin Gong, Ling Wang, Yaochu Jin
, Available online  
Abstract:
Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually difficult to select suitable operators for the problem at hand. Hence, improving operator selection is promising and necessary for CMOEAs. This work proposes an online operator selection framework assisted by Deep Reinforcement Learning. The dynamics of the population, including convergence, diversity, and feasibility, are regarded as the state; the candidate operators are considered as actions; and the improvement of the population state is treated as the reward. By using a Q-network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance. The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems. The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.
Computational Experiments for Complex Social Systems: Experiment Design and Generative Explanation
Xiao Xue, Deyu Zhou, Xiangning Yu, Gang Wang, Juanjuan Li, Xia Xie, Lizhen Cui, Feiyue Wang
, Available online  
Abstract:
Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber-physical-social systems (CPSS). In this context, computational experiments method has emerged as a novel approach for the design, analysis, management, control, and integration of CPSS, which can realize the causal analysis of complex systems by means of “algorithmization” of “counterfactuals”. However, because CPSS involve human and social factors (e.g., autonomy, initiative, and sociality), it is difficult for traditional design of experiment (DOE) methods to achieve the generative explanation of system emergence. To address this challenge, this paper proposes an integrated approach to the design of computational experiments, incorporating three key modules: 1) Descriptive module: Determining the influencing factors and response variables of the system by means of the modeling of an artificial society; 2) Interpretative module: Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena; 3) Predictive module: Building a meta-model that is equivalent to artificial society to explore its operating laws. Finally, a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach, which can reveal the social impact of algorithmic behavior on “rider race”.
Privacy-Preserving Consensus-Based Distributed Economic Dispatch of Smart Grids via State Decomposition
Wei Chen, Guo-Ping Liu
, Available online  , doi: 10.1109/JAS.2023.124122
Abstract:
This paper studies the privacy-preserving distributed economic dispatch (DED) problem of smart grids. An autonomous consensus-based algorithm is developed via local data exchange with neighboring nodes, which covers both the islanded mode and the grid-connected mode of smart grids. To prevent power-sensitive information from being disclosed, a privacy-preserving mechanism is integrated into the proposed DED algorithm by randomly decomposing the state into two parts, where only partial data is transmitted. Our objective is to develop a privacy-preserving DED algorithm to achieve optimal power dispatch with the lowest generation cost under physical constraints while preventing sensitive information from being eavesdropped. To this end, a comprehensive analysis framework is established to ensure that the proposed algorithm can converge to the optimal solution of the concerned optimization problem by means of the consensus theory and the eigenvalue perturbation approach. In particular, the proposed autonomous algorithm can achieve a smooth transition between the islanded mode and the grid-connected mode. Furthermore, rigorous analysis is given to show privacy-preserving performance against internal and external eavesdroppers. Finally, case studies illustrate the feasibility and validity of the developed algorithm.
Goal-Oriented Control Systems (GOCS): From HOW to WHAT
Wen-Hua Chen
, Available online  , doi: 10.1109/JAS.2024.124323
Abstract:
Computational Experiments for Complex Social Systems Part IV: Integrated Design of Experiment System
Xiao Xue, Xiangning Yu, Deyu Zhou, Xiao Wang, Chongke Bi, Shufang Wang, Fei-Yue Wang
, Available online  , doi: 10.1109/JAS.2023.123639
Abstract:
Powered by advanced information industry and intelligent technology, more and more complex systems are exhibiting characteristics of the cyber-physical-social systems (CPSS). And human factors have become crucial in the operations of complex social systems. Traditional mechanical analysis and social simulations alone are powerless for analyzing complex social systems. Against this backdrop, computational experiments have emerged as a new method for quantitative analysis of complex social systems by combining social simulation (e.g., ABM), complexity science, and domain knowledge. However, in the process of applying computational experiments, the construction of experiment system not only considers a large number of artificial society models, but also involves a large amount of data and knowledge. As a result, how to integrate various data, model and knowledge to achieve a running experiment system has become a key challenge. This paper proposes an integrated design framework of computational experiment system, which is composed of four parts: generation of digital subject, generation of digital object, design of operation engine, and construction of experiment system. Finally, this paper outlines a typical case study of coal mine emergency management to verify the validity of the proposed framework.
A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface: Starlight High-Temperature Industrial Endoscope
Zhipeng Chen, Xinyi Wang, Weihua Gui, Jilin Zhu, Chunhua Yang, Zhaohui Jiang
, Available online  
Abstract:
Blast furnace (BF) burden surface contains the most abundant, intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control, optimize gas flow distribution and improve ironmaking efficiency. It has been challengeable to obtain high-quality optical burden surface images under high-temperature, high-dust, and extremely-dim (less than 0.001 Lux) environment. Based on a novel endoscopic sensing detection idea, a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed. Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system, a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment. Based on an endoscope imaging area model, a material flow trajectory model and a gas-dust coupling distribution model, an optimal installation position and posture configuration method for the endoscope is proposed, which maximizes the effective imaging area and ensures large-area, safe and stable imaging of the device in a confined space. Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions, providing key data support for green iron smelting.
Adaptive Trajectory Tracking Control for Non-holonomic Wheeled Mobile Robots: A Barrier Function Sliding Mode Approach
Yunjun Zheng, Jinchuan Zheng, Ke Shao, Han Zhao, Hao Xie, Hai Wang
, Available online  , doi: 10.1109/JAS.2023.124002
Abstract:
The trajectory tracking control performance of nonholonomic wheeled mobile robots (NWMRs) is subject to nonholonomic constraints, system uncertainties, and external disturbances. This paper proposes a barrier function-based adaptive sliding mode control (BFASMC) method to provide high-precision, fast-response performance and robustness for NWMRs. Compared with the conventional adaptive sliding mode control, the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds. Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function. The benefit is that the overestimation of control gain can be eliminated, resulting in chattering reduction. Moreover, a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator. The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the pre-specified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.
Lyapunov Conditions for Finite-Time Input-to-State Stability of Impulsive Switched Systems
Taixiang Zhang, Jinde Cao, Xiaodi Li
, Available online  , doi: 10.1109/JAS.2023.123888
Abstract:
A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration
Yong-Chao Li, Rui-Sheng Jia, Ying-Xiang Hu, Hong-Mei Sun
, Available online  
Abstract:
In a crowd density estimation dataset, the annotation of crowd locations is an extremely laborious task, and they are not taken into the evaluation metrics. In this paper, we aim to reduce the annotation cost of crowd datasets, and propose a crowd density estimation method based on weakly-supervised learning, in the absence of crowd position supervision information, which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information. For this purpose, we design a new training method, which exploits the correlation between global and local image features by incremental learning to train the network. Specifically, we design a parent-child network (PC-Net) focusing on the global and local image respectively, and propose a linear feature calibration structure to train the PC-Net simultaneously, and the child network learns feature transfer factors and feature bias weights, and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network, to improve the convergence of the network by using local features hidden in the crowd images. In addition, we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels, and design a global-local feature loss function (L2). We combine it with a crowd counting loss (LC) to enhance the sensitivity of the network to crowd features during the training process, which effectively improves the accuracy of crowd density estimation. The experimental results show that the PC-Net significantly reduces the gap between fully-supervised and weakly-supervised crowd density estimation, and outperforms the comparison methods on five datasets of ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50, UCF_QNRF and JHU-CROWD++.
Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation
Bin Yang, Yaguo Lei, Xiang Li, Naipeng Li, Asoke K. Nandi
, Available online  
Abstract:
The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain. However, in engineering scenarios, achieving such high-quality label annotation is difficult and expensive. The incorrect label annotation produces two negative effects: 1) the complex decision boundary of diagnosis models lowers the generalization performance on the target domain, and 2) the distribution of target domain samples becomes misaligned with the false-labeled samples. To overcome these negative effects, this article proposes a solution called the label recovery and trajectory designable network (LRTDN). LRTDN consists of three parts. First, a residual network with dual classifiers is to learn features from cross-domain samples. Second, an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain. With the training of relabeled samples, the complexity of diagnosis model is reduced via semi-supervised learning. Third, the adaptation trajectories are designed for sample distributions across domains. This ensures that the target domain samples are only adapted with the pure-labeled samples. The LRTDN is verified by two case studies, in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines. The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.
Cybersecurity Landscape on Remote State Estimation: A Comprehensive Review
Jing Zhou, Jun Shang, Tongwen Chen
, Available online  , doi: 10.1109/JAS.2024.124257
Abstract:
Cyber-physical systems (CPSs) have emerged as an essential area of research in the last decade, providing a new paradigm for the integration of computational and physical units in modern control systems. Remote state estimation (RSE) is an indispensable functional module of CPSs. Recently, it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE, leading to severe estimation performance degradation. This paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures, with a specific focus on integrity attacks against RSE. Firstly, two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed, which provide a deeper insight into the vulnerabilities of RSE. Secondly, a detailed review of typical attack detection and resilient estimation algorithms is included, illustrating the latest defensive measures safeguarding RSE from adversaries. Thirdly, some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers’ and defenders’ perspectives. Finally, several challenges and open problems are presented to inspire further exploration and future research in this field.
Attack-Resilient Distributed Cooperative Control of Virtually Coupled High-Speed Trains via Topology Reconfiguration
Shunyuan Xiao, Xiaohua Ge, Qing Wu
, Available online  
Abstract:
3D Localization for Multiple AUVs in Anchor-Free En-vironments by Exploring the Use of Depth Information
Yichen Li, Wenbin Yu, Xinping Guan
, Available online  , doi: 10.1109/JAS.2023.123261
Abstract:
Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer
Chi Ma, Dianbiao Dong
, Available online  , doi: 10.1109/JAS.2023.123615
Abstract:
This paper studies the problem of time-varying formation control with finite-time prescribed performance for non-strict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities. To eliminate nonlinearities, neural networks are applied to approximate the inherent dynamics of the system. In addition, due to the limitations of the actual working conditions, each follower agent can only obtain the locally measurable partial state information of the leader agent. To address this problem, a neural network state observer based on the leader state information is designed. Then, a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region, which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time. Finally, a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.
Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks
Tao Wang, Qiming Chen, Xun Lang, Lei Xie, Peng Li, Hongye Su
, Available online  
Abstract:
Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability. Although numerous automatic detection techniques have been proposed, most of them can only address part of the practical difficulties. An oscillation is heuristically defined as a visually apparent periodic variation. However, manual visual inspection is labor-intensive and prone to missed detection. Convolutional neural networks (CNNs), inspired by animal visual systems, have been raised with powerful feature extraction capabilities. In this work, an exploration of the typical CNN models for visual oscillation detection is performed. Specifically, we tested MobileNet-V1, ShuffleNet-V2, EfficientNet-B0, and GhostNet models, and found that such a visual framework is well-suited for oscillation detection. The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases. Compared with state-of-the-art oscillation detectors, the suggested framework is more straightforward and more robust to noise and mean-nonstationarity. In addition, this framework generalizes well and is capable of handling features that are not present in the training data, such as multiple oscillations and outliers.
Parameter-Free Shifted Laplacian Reconstruction for Multiple Kernel Clustering
Xi Wu, Zhenwen Ren, F. Richard Yu
, Available online  , doi: 10.1109/JAS.2023.123600
Abstract:
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview
Wenqi Ren, Yang Tang, Qiyu Sun, Chaoqiang Zhao, Qing-Long Han
, Available online  , doi: 10.1109/JAS.2023.123207
Abstract:
Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block, and it plays a crucial role in environmental perception. Conventional learning-based visual semantic segmentation approaches count heavily on large-scale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories. This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning. The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples, which advances the extension to practical applications. Therefore, this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances. Specifically, the preliminaries on few/zero-shot visual semantic segmentation, including the problem definitions, typical datasets, and technical remedies, are briefly reviewed and discussed. Moreover, three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation, including image semantic segmentation, video object segmentation, and 3D segmentation. Finally, the future challenges of few/zero-shot visual semantic segmentation are discussed.
Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control
Hao Zhang, Yan Li, Zhuping Wang, Yi Ding, Huaicheng Yan
, Available online  , doi: 10.1109/JAS.2023.123381
Abstract:
Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults
Li-Ying Hao, Gege Dong, Tieshan Li, Zhouhua Peng
, Available online  , doi: 10.1109/JAS.2023.123675
Abstract:
This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults, uncertainty and external disturbances. Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments, which may cause existing obstacle avoidance strategies to fail. To reduce the influence of actuator faults, an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors. The nonlinear state observer, which only depends on measurable position information of the autonomous surface vehicle, is used to address uncertainties and external disturbances. By using a backstepping technique and adaptive mechanism, a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero. Compared with existing results, the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously. Finally, the comparison results through simulations are given to verify the effectiveness of the proposed method.
A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends
M. Victoria Luzón, Nuria Rodríguez-Barroso, Alberto Argente-Garrido, Daniel Jiménez-López, Jose M. Moyano, Javier Del Ser, Weiping Ding, Francisco Herrera
, Available online  , doi: 10.1109/JAS.2024.124215
Abstract:
When data privacy is imposed as a necessity, Federated learning (FL) emerges as a relevant artificial intelligence field for developing machine learning (ML) models in a distributed and decentralized environment. FL allows ML models to be trained on local devices without any need for centralized data transfer, thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties. This paradigm has gained momentum in the last few years, spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources. By virtue of FL, models can be learned from all such distributed data sources while preserving data privacy. The aim of this paper is to provide a practical tutorial on FL, including a short methodology and a systematic analysis of existing software frameworks. Furthermore, our tutorial provides exemplary cases of study from three complementary perspectives: i) Foundations of FL, describing the main components of FL, from key elements to FL categories; ii) Implementation guidelines and exemplary cases of study, by systematically examining the functionalities provided by existing software frameworks for FL deployment, devising a methodology to design a FL scenario, and providing exemplary cases of study with source code for different ML approaches; and iii) Trends, shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape. The ultimate purpose of this work is to establish itself as a referential work for researchers, developers, and data scientists willing to explore the capabilities of FL in practical applications.
Quantization and Event-Triggered Policy Design for Encrypted Networked Control
Yongxia Shi, Ehsan Nekouei
, Available online  
Abstract:
This paper proposes a novel event-driven encrypted control framework for linear networked control systems (NCSs), which relies on two modified uniform quantization policies, the Paillier cryptosystem, and an event-triggered strategy. Due to the fact that only integers can work in the Pailler cryptosystem, both the real-valued control gain and system state need to be first quantized before encryption. This is dramatically different from the existing quantized control methods, where only the quantization of a single value, e.g., the control input or the system state, is considered. To handle this issue, static and dynamic quantization policies are presented, which achieve the desired integer conversions and guarantee asymptotic convergence of the quantized system state to the equilibrium. Then, the quantized system state is encrypted and sent to the controller when the triggering condition, specified by a state-based event-triggered strategy, is satisfied. By doing so, not only the security and confidentiality of data transmitted over the communication network are protected, but also the ciphertext expansion phenomenon can be relieved. Additionally, by tactfully designing the quantization sensitivities and triggering error, the proposed event-driven encrypted control framework ensures the asymptotic stability of the overall closed-loop system. Finally, a simulation example of the secure motion control for an inverted pendulum cart system is presented to evaluate the effectiveness of the theoretical results.
Side Information-Based Stealthy False Data Injection Attacks Against Multi-Sensor Remote Estimation
Haibin Guo, Zhong-Hua Pang, Chao Li
, Available online  
Abstract:
Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance
Elham Javanfar, Mehdi Rahmani
, Available online  , doi: 10.1109/JAS.2023.124164
Abstract:
This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise. The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system. Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering, we first propose the weighted sum of norms (SON) clustering method that prioritizes nearby points, reduces distant point influence, and lowers computational cost. Then, by introducing the weighted maximum likelihood, we propose a semi-definite program (SDP) to detect outliers and reduce their impacts on each cluster. Detecting these weights paves the way to obtain an appropriate covariance of the output noise. Next, two filtering approaches are presented: a cluster-based robust linear filter using the maximum a posterior (MAP) estimation and a cluster-based robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters. At last, simulation results demonstrate the effectiveness of our proposed filtering approaches.
Adaptive Space Expansion for Fast Motion Planning
Shenglei Shi, Jiankui Chen
, Available online  , doi: 10.1109/JAS.2023.123765
Abstract:
The sampling process is very inefficient for sampling-based motion planning algorithms that excess random samples are generated in the planning space. In this paper, we propose an adaptive space expansion (ASE) approach which belongs to the informed sampling category to improve the sampling efficiency for quickly finding a feasible path. The ASE method enlarges the search space gradually and restrains the sampling process in a sequence of small hyper-ellipsoid ring subsets to avoid exploring the unnecessary space. Specifically, for a constructed small hyper-ellipsoid ring subset, if the algorithm cannot find a feasible path in it, then the subset is expanded. Thus, the ASE method successively does space exploring and space expansion until the final path has been found. Besides, we present a particular construction method of the hyper-ellipsoid ring that uniform random samples can be directly generated in it. At last, we present a feasible motion planner BiASE and an asymptotically optimal motion planner BiASE* using the bidirectional exploring method and the ASE strategy. Simulations demonstrate that the computation speed is much faster than that of the state-of-the-art algorithms. The source codes are available at https://github.com/shshlei/ompl.
Observer-Based Adaptive Robust Precision Motion Control of a Multi-Joint Hydraulic Manipulator
Zheng Chen, Shizhao Zhou, Chong Shen, Litong Lyu, Junhui Zhang, Bin Yao
, Available online  , doi: 10.1109/JAS.2024.124209
Abstract:
Hydraulic manipulators are usually applied in heavy-load and harsh operation tasks. However, when faced with a complex operation, the traditional proportional-integral-derivative (PID) control may not meet requirements for high control performance. Model-based full-state-feedback control is an effective alternative, but the states of a hydraulic manipulator are not always available and reliable in practical applications, particularly the joint angular velocity measurement. Considering that it is not suitable to obtain the velocity signal directly from differentiating of position measurement, the low-pass filtering is commonly used, but it will definitely restrict the closed-loop bandwidth of the whole system. To avoid this problem and realize better control performance, this paper proposes a novel observer-based adaptive robust controller (obARC) for a multi-joint hydraulic manipulator subjected to both parametric uncertainties and the lack of accurate velocity measurement. Specifically, a nonlinear adaptive observer is first designed to handle the lack of velocity measurement with the consideration of parametric uncertainties. Then, the adaptive robust control is developed to compensate for the dynamic uncertainties, and the close-loop system robust stability is theoretically proved under the observation and control errors. Finally, comparative experiments are carried out to show that the designed controller can achieve a performance improvement over the traditional methods, specifically yielding better control accuracy owing to the closed-loop bandwidth breakthrough, which is limited by low-pass filtering in full-state-feedback control.
Relaxed Stability Criteria for Time-Delay Systems: A Novel Quadratic Function Convex Approximation Approach
Shenquan Wang, Wenchengyu Ji, Yulian Jiang, Yanzheng Zhu, Jian Sun
, Available online  , doi: 10.1109/JAS.2023.123735
Abstract:
This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying delays. By introducing two adjustable parameters and two free variables, a novel convex function greater than or equal to the quadratic function is constructed, regardless of the sign of the coefficient in the quadratic term. The developed lemma can also be degenerated into the existing quadratic function negative-determination (QFND) lemma and relaxed QFND lemma respectively, by setting two adjustable parameters and two free variables as some particular values. Moreover, for a linear system with time-varying delays, a relaxed stability criterion is established via our developed lemma, together with the quivalent reciprocal combination technique and the Bessel-Legendre inequality. As a result, the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay systems. Finally, the superiority of our results is illustrated through three numerical examples.
Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells
Meng Zhou, Zihao Wang, Jing Wang, Zhengcai Cao
, Available online  , doi: 10.1109/JAS.2023.124041
Abstract:
This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine (SVM) based on the definition of buffered Voronoi cells (BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm. Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.
Approximately Bi-Similar Symbolic Model for Discrete-time Interconnected Switched System
Yang Song, Yongzhuang Liu, Wanqing Zhao
, Available online  , doi: 10.1109/JAS.2023.123927
Abstract:
Distributed Finite-Time Event-Triggered Formation Control Based on a Unified Framework of Affine Image
Yan-Jun Lin, Yun-Shi Yang, Li Chai, Zhi-Yun Lin
, Available online  , doi: 10.1109/JAS.2023.123885
Abstract:
Event-Triggered Fault Detection — An Integrated Design Approach Directly Toward Fault Diagnosis Performance
Aibing Qiu, Yu Hu, Jingsong Wu
, Available online  , doi: 10.1109/JAS.2023.124074
Abstract:
Global Stabilization Via Adaptive Event-Triggered Output Feedback for Nonlinear Systems With Unknown Measurement Sensitivity
Yupin Wang, Hui Li
, Available online  , doi: 10.1109/JAS.2023.123984
Abstract:
Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication: Progress, Insights and Trends
Weihao Song, Zidong Wang, Zhongkui Li, Jianan Wang, Qing-Long Han
, Available online  , doi: 10.1109/JAS.2023.123588
Abstract:
The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance. The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation, cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many sample-based nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter, and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security. Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.
Synchronous Membership Function Dependent Event-Triggered H Control of T-S Fuzzy Systems Under Network Communications
Bo-Lin Xu, Chen Peng, Wen-Bo Xie
, Available online  , doi: 10.1109/JAS.2023.123729
Abstract:
Adaptive Consensus of Uncertain Multi-Agent Systems With Unified Prescribed Performance
Kun Li, Kai Zhao, Yongduan Song
, Available online  , doi: 10.1109/JAS.2023.123723
Abstract:
A Novel Scalable Fault-Tolerant Control Design for DC Microgrids WIth Nonuniform Faults
Aimin Wang, Minrui Fei, Dajun Du, Yang Song
, Available online  , doi: 10.1109/JAS.2023.123918
Abstract:
Intra-independent Distributed Resource Allocation Game
Jialing Zhou, Guanghui Wen, Yuezu Lv, Tao Yang, Guanrong Chen
, Available online  , doi: 10.1109/JAS.2023.123906
Abstract:
A Multi-AGV Routing Planning Method Based on Deep Reinforcement Learning and Recurrent Neural Network
Yishuai Lin, Gang Hu, Liang Wang, Qingshan Li, Jiawei Zhu
, Available online  , doi: 10.1109/JAS.2023.123300
Abstract:
Integrating Inventory Monitoring and Capacity Changes in Dynamic Supply Chains with Bi-Directional Cascading Propagation Effects
En-Zhi Cao, Chen Peng, Qing-Kui Li
, Available online  , doi: 10.1109/JAS.2023.123309
Abstract:
Recurrent Neural Network Inspired Finite-Time Control Design
Jianan Liu, Shihua Li, Rongjie Liu
, Available online  , doi: 10.1109/JAS.2023.123297
Abstract:
Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
Abstract:
Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey
Minfeng Qi, Ziyuan Wang, Qing-Long Han, Jun Zhang, Shiping Chen, Yang Xiang
, Available online  , doi: 10.1109/JAS.2022.106058
Abstract:
To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.
Distributed Minimum-Energy Containment Control of Continuous-Time Multi-Agent Systems by Inverse Optimal Control
Fei Yan, Xiangbiao Liu, Tao Feng
, Available online  , doi: 10.1109/JAS.2022.106067
Abstract:
Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers
Meiling Xie, Derui Ding, Xiaohua Ge, Qing-Long Han, Hongli Dong, Yan Song
, Available online  , doi: 10.1109/JAS.2022.105941
Abstract:
Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
Supplementary File of “Push-Sum Based Algorithm for Constrained Convex Optimization Problem and Its Potential Application in Smart Grid”
Qian Xu, Zao Fu, Bo Zou, Hongzhe Liu, Lei Wang
, Available online  
Abstract:
Supplementary Material for “Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes”
Zichao Xing, Xinyu Chen, Xingkai Wang, Weimin Wu, Ruifen Hu
, Available online  
Abstract: