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. 10,  No. 2, 2023

Display Method:
The Distribution of Zeros of Quasi-Polynomials
Honghai Wang, Qing-Long Han
2023, 10(2): 301-304. doi: 10.1109/JAS.2023.123597
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A Survey on Negative Transfer
Wen Zhang, Lingfei Deng, Lei Zhang, Dongrui Wu
2023, 10(2): 305-329. doi: 10.1109/JAS.2022.106004
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Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces learning performance in the target domain, and has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to address this issue. However, there does not exist a systematic survey. This paper fills this gap, by first introducing the definition of NT and its causes, and reviewing over fifty representative approaches for overcoming NT, which fall into three categories: domain similarity estimation, safe transfer, and NT mitigation. Many areas, including computer vision, bioinformatics, natural language processing, recommender systems, and robotics, that use NT mitigation strategies to facilitate positive transfers, are also reviewed. Finally, we give guidelines on NT task construction and baseline algorithms, benchmark existing TL and NT mitigation approaches on three NT-specific datasets, and point out challenges and future research directions. To ensure reproducibility, our code is publicized at https://github.com/chamwen/NT-Benchmark.
Three-Way Behavioral Decision Making With Hesitant Fuzzy Information Systems: Survey and Challenges
Jianming Zhan, Jiajia Wang, Weiping Ding, Yiyu Yao
2023, 10(2): 330-350. doi: 10.1109/JAS.2022.106061
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Three-way decision (T-WD) theory is about thinking, problem solving, and computing in threes. Behavioral decision making (BDM) focuses on effective, cognitive, and social processes employed by humans for choosing the optimal object, of which prospect theory and regret theory are two widely used tools. The hesitant fuzzy set (HFS) captures a series of uncertainties when it is difficult to specify precise fuzzy membership grades. Guided by the principles of three-way decisions as thinking in threes and integrating these three topics together, this paper reviews and examines advances in three-way behavioral decision making (TW-BDM) with hesitant fuzzy information systems (HFIS) from the perspective of the past, present, and future. First, we provide a brief historical account of the three topics and present basic formulations. Second, we summarize the latest development trends and examine a number of basic issues, such as one-sidedness of reference points and subjective randomness for result values, and then report the results of a comparative analysis of existing methods. Finally, we point out key challenges and future research directions.
Data-Driven Control of Distributed Event-Triggered Network Systems
Xin Wang, Jian Sun, Gang Wang, Frank Allgöwer, Jie Chen
2023, 10(2): 351-364. doi: 10.1109/JAS.2023.123225
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The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems (a.k.a. network systems). To this end, we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling, under which a model-based stability criterion for the closed-loop network system is derived, by leveraging a discrete-time looped-functional approach. Marrying the model-based criterion with a data-driven system representation recently developed in the literature, a purely data-driven stability criterion expressed in the form of linear matrix inequalities (LMIs) is established. Meanwhile, the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data. Finally, numerical results corroborate the efficacy of the proposed distributed data-driven event-triggerednetwork system (ETS) in cutting off data transmissions and the co-design procedure.
Driver Intent Prediction and Collision Avoidance With Barrier Functions
Yousaf Rahman, Abhishek Sharma, Mrdjan Jankovic, Mario Santillo, Michael Hafner
2023, 10(2): 365-375. doi: 10.1109/JAS.2023.123210
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For autonomous vehicles and driver assist systems, path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers. In the literature, the algorithms that provide driver intent belong to two categories: those that use physics based models with some type of filtering, and machine learning based approaches. In this paper we employ barrier functions (BF) to decide driver intent. BFs are typically used to prove safety by establishing forward invariance of an admissible set. Here, we decide if the “target” vehicle is violating one or more possibly fictitious (i.e., non-physical) barrier constraints determined based on the context provided by the road geometry. The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives. The predicted intent is then used by a control barrier function (CBF) based collision avoidance system to prevent unnecessary interventions, for either an autonomous or human-driven vehicle.
Distributed Nash Equilibrium Seeking for General Networked Games With Bounded Disturbances
Maojiao Ye, Danhu Li, Qing-Long Han, Lei Ding
2023, 10(2): 376-387. doi: 10.1109/JAS.2022.105428
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This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial information. First, reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with first-order and second-order players, respectively. In the developed algorithms, the observed disturbance values are included in control signals to eliminate the influence of disturbances, based on which a gradient-like optimization method is implemented for each player. Second, a signum function based distributed algorithm is proposed to attenuate disturbances for games with second-order integrator-type players. To be more specific, a signum function is involved in the proposed seeking strategy to dominate disturbances, based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players’ objective functions. Through Lyapunov stability analysis, it is proven that the players’ actions can approach a small region around the Nash equilibrium by utilizing disturbance observer-based strategies with appropriate control gains. Moreover, exponential (asymptotic) convergence can be achieved when the signum function based control strategy (with an adaptive control gain) is employed. The performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform (V-REP) and MATLAB.
Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays
Xian Yang, Jing Yan, Changchun Hua, Xinping Guan
2023, 10(2): 388-402. doi: 10.1109/JAS.2022.106076
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Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects remotely. For the tracking control of teleoperation systems, velocity measurements are necessary to provide feedback information. However, due to hardware technology and cost constraints, the velocity measurements are not always available. In addition, the time-varying communication delay makes it challenging to achieve tracking task. This paper provides a solution to the issue of real-time tracking for teleoperation systems, subjected to unavailable velocity signals and time-varying communication delays. In order to estimate the velocity information, immersion and invariance (I&I) technique is employed to develop an exponential stability velocity observer. For the proposed velocity observer, a linear relationship between position and observation state is constructed, through which the need of solving partial differential and certain integral equations can be avoided. Meanwhile, the mean value theorem is exploited to separate the observation error terms, and hence, all functions in our observer can be analytically expressed. With the estimated velocity information, a slave-torque feedback control law is presented. A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking conditions. In particular, the relationship between the controller design parameters and the allowable maximum delay values is provided. Finally, simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.
Fixed-Time Stabilization of a Class of Strict-Feedback Nonlinear Systems via Dynamic Gain Feedback Control
Chenghui Zhang, Le Chang, Lantao Xing, Xianfu Zhang
2023, 10(2): 403-410. doi: 10.1109/JAS.2023.123408
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This paper presents a novel fixed-time stabilization control (FSC) method for a class of strict-feedback nonlinear systems involving unmodelled system dynamics. The key feature of the proposed method is the design of two dynamic parameters. Specifically, a set of auxiliary variables is first introduced through state transformation. These variables combine the original system states and the two introduced dynamic parameters, facilitating the closed-loop system stability analyses. Then, the two dynamic parameters are delicately designed by utilizing the Lyapunov method, ensuring that all the closed-loop system states are globally fixed-time stable. Compared with existing results, the “explosion of complexity” problem of backstepping control is avoided. Moreover, the two designed dynamic parameters are dependent on system states rather than a time-varying function, thus the proposed controller is still valid beyond the given fixed-time convergence instant. The effectiveness of the proposed method is demonstrated through two practical systems.
Loop Closure Detection via Locality Preserving Matching With Global Consensus
Jiayi Ma, Kaining Zhang, Junjun Jiang
2023, 10(2): 411-426. doi: 10.1109/JAS.2022.105926
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A critical component of visual simultaneous localization and mapping is loop closure detection (LCD), an operation judging whether a robot has come to a pre-visited area. Concretely, given a query image (i.e., the latest view observed by the robot), it proceeds by first exploring images with similar semantic information, followed by solving the relative relationship between candidate pairs in the 3D space. In this work, a novel appearance-based LCD system is proposed. Specifically, candidate frame selection is conducted via the combination of Super-features and aggregated selective match kernel (ASMK). We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task. It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance. To dig up consistent geometry between image pairs during loop closure verification, we propose a simple yet surprisingly effective feature matching algorithm, termed locality preserving matching with global consensus (LPM-GC). The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs, where a global constraint is further designed to effectively remove false correspondences in challenging sceneries, e.g., containing numerous repetitive structures. Meanwhile, we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds. The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets. Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks. We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.
Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection
Pan Yu, Kang-Zhi Liu, Xudong Liu, Xiaoli Li, Min Wu, Jinhua She
2023, 10(2): 427-438. doi: 10.1109/JAS.2023.123231
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In this paper, a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph. It is of two-degree-of-freedom nature. Specifically, a robust distributed controller is designed for consensus tracking, while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances. The condition for asymptotic disturbance rejection is derived. Moreover, even when the disturbance model is not exactly known, the developed method also provides good disturbance-rejection performance. Then, a robust stabilization condition with less conservativeness is derived for the whole multi-agent system. Further, a design algorithm is given. Finally, comparisons with the conventional one-degree-of-freedom-based distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.
A Quantum Tanimoto Coefficient Fidelity for Entanglement Measurement
Yangyang Zhao, Fuyuan Xiao, Masayoshi Aritsugi, Weiping Ding
2023, 10(2): 439-450. doi: 10.1109/JAS.2022.106079
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Fidelity plays an important role in quantum information processing, which provides a basic scale for comparing two quantum states. At present, one of the most commonly used fidelities is Uhlmann-Jozsa (U-J) fidelity. However, U-J fidelity needs to calculate the square root of the matrix, which is not trivial in the case of large or infinite density matrices. Moreover, U-J fidelity is a measure of overlap, which has limitations in some cases and cannot reflect the similarity between quantum states well. Therefore, a novel quantum fidelity measure called quantum Tanimoto coefficient (QTC) fidelity is proposed in this paper. Unlike other existing fidelities, QTC fidelity not only considers the overlap between quantum states, but also takes into account the separation between quantum states for the first time, which leads to a better performance of measure. Specifically, we discuss the properties of the proposed QTC fidelity. QTC fidelity is compared with some existing fidelities through specific examples, which reflects the effectiveness and advantages of QTC fidelity. In addition, based on the QTC fidelity, three discrimination coefficients ${\boldsymbol{d_1^{{\bf{QTC}}} }}$, ${\boldsymbol{d_2^{{\bf{QTC}}}}}$, and ${\boldsymbol{d_3^{{\bf{QTC}}}}}$ are defined to measure the difference between quantum states. It is proved that the discrimination coefficient ${\boldsymbol{d_3^{{\bf{QTC}}} }}$ is a true metric. Finally, we apply the proposed QTC fidelity-based discrimination coefficients to measure the entanglement of quantum states to show their practicability.
Adaptive Uniform Performance Control of Strict-Feedback Nonlinear Systems With Time-Varying Control Gain
Kai Zhao, Changyun Wen, Yongduan Song, Frank L. Lewis
2023, 10(2): 451-461. doi: 10.1109/JAS.2022.106064
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In this paper, we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients, which mainly includes the following steps. Firstly, by introducing several key transformation functions and selecting the initial value of the time-varying scaling function, the symmetric prescribed performance with global and semi-global properties can be handled uniformly, without the need for control re-design. Secondly, to handle the problem of unknown time-varying control coefficient with an unknown sign, we propose an enhanced Nussbaum function (ENF) bearing some unique properties and characteristics, with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer required. Thirdly, by utilizing the core-function information technique, the nonparametric uncertainties in the system are gracefully handled so that no approximator is required. Furthermore, simulation results verify the effectiveness and benefits of the approach.
A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge
Bingyun Li, Qinmin Yang, Innocent Kamwa
2023, 10(2): 462-473. doi: 10.1109/JAS.2023.123216
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Demand response (DR) using shared energy storage systems (ESSs) is an appealing method to save electricity bills for users under demand charge and time-of-use (TOU) price. A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study. In this scheme, the interactions between selfish users and an operator are characterized as a Stackelberg game. Operator holds a large-scale ESS that is shared among users in the form of energy transactions. It sells energy to users and sets the selling price first. It maximizes its profit through optimal pricing and ESS dispatching. Users purchase some energy from operator for the reduction of their demand charges after operator’s selling price is announced. This game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization model. The upper-level optimization maximizes operator’s profit and the lower-level optimization minimizes users’ costs. The bi-level model is transformed and linearized into a mixed-integer linear programming (MILP) model using the mathematical programming with equilibrium constraints (MPEC) method and model linearizing techniques. Case studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.
Anti-Disturbance Control for Tethered Aircraft System With Deferred Output Constraints
Mengshi Song, Fan Zhang, Bingxiao Huang, Panfeng Huang
2023, 10(2): 474-485. doi: 10.1109/JAS.2023.123222
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In this paper, we investigate the peaking issue of extended state observers and the anti-disturbance control problem of tethered aircraft systems subject to the unstable flight of the main aircraft, airflow disturbances and deferred output constraints. Independent of exact initial values, a modified extended state observer is constructed from a shifting function such that not only the peaking issue inherently in the observer is circumvented completely but also the accurate estimation of the lumped disturbance is guaranteed. Meanwhile, to deal with deferred output constraints, an improved output constrained controller is employed by integrating the shifting function into the barrier Lyapunov function. Then, by combining the modified observer and the improved controller, an anti-disturbance control scheme is presented, which ensures that the outputs with any bounded initial conditions satisfy the constraints after a pre-specified finite time, and the tethered aircraft tracks the desired trajectory accurately. Finally, both a theoretical proof and simulation results verify the effectiveness of the proposed control scheme.
Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering
Xiaoyao Zheng, Baoting Han, Zhen Ni
2023, 10(2): 486-500. doi: 10.1109/JAS.2023.123219
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Tourism route planning is widely applied in the smart tourism field. The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails, sharp peaks and disconnected regions problems, which leads to uneven distribution and weak diversity of optimization solutions of tourism routes. Inspired by these limitations, we propose a multi-objective evolutionary algorithm for tourism route recommendation (MOTRR) with two-stage and Pareto layering based on decomposition. The method decomposes the multi-objective problem into several subproblems, and improves the distribution of solutions through a two-stage method. The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method. The neighborhood is determined according to the weight of the subproblem for crossover mutation. Finally, Pareto layering is used to improve the updating efficiency and population diversity of the solution. The two-stage method is combined with the Pareto layering structure, which not only maintains the distribution and diversity of the algorithm, but also avoids the same solutions. Compared with several classical benchmark algorithms, the experimental results demonstrate competitive advantages on five test functions, hypervolume (HV) and inverted generational distance (IGD) metrics. Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing, our proposed algorithm shows better distribution. It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity, so that the recommended routes can better meet the personalized needs of tourists.
Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints
Yan Yang, Jiangtao Han, Zhijie Liu, Zhijia Zhao, Keum-Shik Hong
2023, 10(2): 501-511. doi: 10.1109/JAS.2023.123213
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This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm. The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory. The unmodeled dynamics of the system are considered, and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network. The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory. The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.
Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game
Jie Chen, Kaiyi Luo, Changbing Tang, Zhao Zhang, Xiang Li
2023, 10(2): 512-523. doi: 10.1109/JAS.2022.105521
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Weighted vertex cover (WVC) is one of the most important combinatorial optimization problems. In this paper, we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks. We first model the WVC problem as a general game on weighted networks. Under the framework of a game, we newly define several cover states to describe the WVC problem. Moreover, we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums (SNEs) of the game. Then, we propose a game-based asynchronous algorithm (GAA), which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time. Subsequently, we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms, termed the improved game-based asynchronous algorithm (IGAA), in which we prove that it can obtain a better solution to the WVC problem than using a the GAA. Finally, numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.
Deep Domain-Adversarial Anomaly Detection With One-Class Transfer Learning
Wentao Mao, Gangsheng Wang, Linlin Kou, Xihui Liang
2023, 10(2): 524-546. doi: 10.1109/JAS.2023.123228
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Despite the big success of transfer learning techniques in anomaly detection, it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification, especially for the data with a large distribution difference. To address this challenge, a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper. First, by integrating a hypersphere adaptation constraint into domain-adversarial neural network, a new hypersphere adversarial training mechanism is designed. Second, an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible. Through transferring one-class detection rule in the adaptive extraction of domain-invariant feature representation, the end-to-end anomaly detection with one-class classification is then enhanced. Furthermore, a theoretical analysis about the model reliability, as well as the strategy of avoiding invalid and negative transfer, is provided. Experiments are conducted on two typical anomaly detection problems, i.e., image recognition detection and online early fault detection of rolling bearings. The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.
Driver-Centric Velocity Prediction With Multidimensional Fuzzy Granulation
Ji Li, Quan Zhou, Xu He, Hongming Xu
2023, 10(2): 547-549. doi: 10.1109/JAS.2022.105998
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Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised Data-Driven Approach
Zhenyong Zhang, Yan Qin, Jingpei Wang, Hui Li, Ruilong Deng
2023, 10(2): 550-553. doi: 10.1109/JAS.2023.123243
Abstract(187) HTML (36) PDF(24)
A Distributed Self-Consistent Control Method for Electric Vehicles to Coordinate Low-Carbon Transportation and Energy
Bowen Zhou, Chao Xi, Dongsheng Yang, Qiuye Sun, Huaguang Zhang
2023, 10(2): 554-556. doi: 10.1109/JAS.2023.123240
Abstract(204) HTML (57) PDF(27)
CoRE: Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power Systems
Zhenyong Zhang, David K. Y. Yau
2023, 10(2): 557-559. doi: 10.1109/JAS.2023.123252
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Dynamic Target Enclosing Control Scheme for Multi-Agent Systems via a Signed Graph-Based Approach
Weihao Li, Kaiyu Qin, Mengji Shi, Jingliang Shao, Boxian Lin
2023, 10(2): 560-562. doi: 10.1109/JAS.2023.123234
Abstract(186) HTML (43) PDF(33)
Optimal Formation Control for Second-Order Multi-Agent Systems With Obstacle Avoidance
Jiaxin Zhang, Wei Liu, Yongming Li
2023, 10(2): 563-565. doi: 10.1109/JAS.2023.123249
Abstract(243) HTML (36) PDF(58)
Visual Feedback Disturbance Rejection Control for an Amphibious Bionic Stingray Under Actuator Saturation
Haiyan Cheng, Bin Fang, Qing Liu, Jinhua Zhang, Jun Hong
2023, 10(2): 566-568. doi: 10.1109/JAS.2023.123237
Abstract(185) HTML (39) PDF(20)
Prescribed-Time Stabilization of Singularly Perturbed Systems
Yan Lei, Yan-Wu Wang, Xiao-Kang Liu, Wu Yang
2023, 10(2): 569-571. doi: 10.1109/JAS.2023.123246
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Straight-Path Following and Formation Control of USVs Using Distributed Deep Reinforcement Learning and Adaptive Neural Network
Zhengqing Han, Yintao Wang, Qi Sun
2023, 10(2): 572-574. doi: 10.1109/JAS.2023.123255
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