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. 6, 2022

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Large-Scale Group Decision Making: A Systematic Review and a Critical Analysis
Diego García-Zamora, Álvaro Labella, Weiping Ding, Rosa M. Rodríguez, Luis Martínez
2022, 9(6): 949-966. doi: 10.1109/JAS.2022.105617
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The society in the digital transformation era demands new decision schemes such as e-democracy or based on social media. Such novel decision schemes require the participation of many experts/decision makers/stakeholders in the decision processes. As a result, large-scale group decision making (LSGDM) has attracted the attention of many researchers in the last decade and many studies have been conducted in order to face the challenges associated with the topic. Therefore, this paper aims at reviewing the most relevant studies about LSGDM, identifying the most profitable research trends and analyzing them from a critical point of view. To do so, the Web of Science database has been consulted by using different searches. From these results a total of 241 contributions were found and a selection process regarding language, type of contribution and actual relation with the studied topic was then carried out. The 87 contributions finally selected for this review have been analyzed from four points of view that have been highly remarked in the topic, such as the preference structure in which decision-makers’ opinions are modeled, the group decision rules used to define the decision making process, the techniques applied to verify the quality of these models and their applications to real world problems solving. Afterwards, a critical analysis of the main limitations of the existing proposals is developed. Finally, taking into account these limitations, new research lines for LSGDM are proposed and the main challenges are stressed out.
Wearable Robots for Human Underwater Movement Ability Enhancement: A Survey
Haisheng Xia, Muhammad Alamgeer Khan, Zhijun Li, MengChu Zhou
2022, 9(6): 967-977. doi: 10.1109/JAS.2022.105620
Abstract(7096) HTML (110) PDF(135)
Underwater robot technology has shown impressive results in applications such as underwater resource detection. For underwater applications that require extremely high flexibility, robots cannot replace skills that require human dexterity yet, and thus humans are often required to directly perform most underwater operations. Wearable robots (exoskeletons) have shown outstanding results in enhancing human movement on land. They are expected to have great potential to enhance human underwater movement. The purpose of this survey is to analyze the state-of-the-art of underwater exoskeletons for human enhancement, and the applications focused on movement assistance while excluding underwater robotic devices that help to keep the temperature and pressure in the range that people can withstand. This work discusses the challenges of existing exoskeletons for human underwater movement assistance, which mainly includes human underwater motion intention perception, underwater exoskeleton modeling and human-cooperative control. Future research should focus on developing novel wearable robotic structures for underwater motion assistance, exploiting advanced sensors and fusion algorithms for human underwater motion intention perception, building up a dynamic model of underwater exoskeletons and exploring human-in-the-loop control for them.
Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems With the Denial-of-Service Attacks and Stochastic Communication Protocol
Hang Geng, Zidong Wang, Yun Chen, Xiaojian Yi, Yuhua Cheng
2022, 9(6): 978-989. doi: 10.1109/JAS.2022.105623
Abstract(758) HTML (272) PDF(107)
In this paper, a new filtering fusion problem is studied for nonlinear cyber-physical systems under error-variance constraints and denial-of-service attacks. To prevent data collision and reduce communication cost, the stochastic communication protocol is adopted in the sensor-to-filter channels to regulate the transmission order of sensors. Each sensor is allowed to enter the network according to the transmission priority decided by a set of independent and identically-distributed random variables. From the defenders’ view, the occurrence of the denial-of-service attack is governed by the randomly Bernoulli-distributed sequence. At the local filtering stage, a set of variance-constrained local filters are designed where the upper bounds (on the filtering error covariances) are first acquired and later minimized by appropriately designing filter parameters. At the fusion stage, all local estimates and error covariances are combined to develop a variance-constrained fusion estimator under the federated fusion rule. Furthermore, the performance of the fusion estimator is examined by studying the boundedness of the fused error covariance. A simulation example is finally presented to demonstrate the effectiveness of the proposed fusion estimator.
A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance
Xiaohua Ge, Qing-Long Han, Jun Wang, Xian-Ming Zhang
2022, 9(6): 990-1004. doi: 10.1109/JAS.2021.1004263
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This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.
Fixed-Time Lyapunov Criteria and State-Feedback Controller Design for Stochastic Nonlinear Systems
Huifang Min, Shengyuan Xu, Baoyong Zhang, Qian Ma, Deming Yuan
2022, 9(6): 1005-1014. doi: 10.1109/JAS.2022.105539
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This paper investigates the fixed-time stability theorem and state-feedback controller design for stochastic nonlinear systems. We propose an improved fixed-time Lyapunov theorem with a more rigorous and reasonable proof procedure. In particular, an important corollary is obtained, which can give a less conservative upper-bound estimate of the settling time. Based on the backstepping technique and the addition of a power integrator method, a state-feedback controller is skillfully designed for a class of stochastic nonlinear systems. It is proved that the proposed controller can render the closed-loop system fixed-time stable in probability with the help of the proposed fixed-time stability criteria. Finally, the effectiveness of the proposed controller is demonstrated by simulation examples and comparisons.
A Telepresence-Guaranteed Control Scheme for Teleoperation Applications of Transferring Weight-Unknown Objects
Jinfei Hu, Zheng Chen, Xin Ma, Han Lai, Bin Yao
2022, 9(6): 1015-1025. doi: 10.1109/JAS.2022.105626
Abstract(668) HTML (143) PDF(74)
Currently, most teleoperation work is focusing on scenarios where slave robots interact with unknown environments. However, in some fields such as medical robots or rescue robots, the other typical teleoperation application is precise object transportation. Generally, the object’s weight is unknown yet essential for both accurate control of the slave robot and intuitive perception of the human operator. However, due to high cost and limited installation space, it is unreliable to employ a force sensor to directly measure the weight. Therefore, in this paper, a control scheme free of force sensor is proposed for teleoperation robots to transfer a weight-unknown object accurately. In this scheme, the workspace mapping between master and slave robot is firstly established, based on which, the operator can generate command trajectory on-line by operating the master robot. Then, a slave controller is designed to follow the master command closely and estimate the object’s weight rapidly, accurately and robust to unmodeled uncertainties. Finally, for the sake of telepresence, a master controller is designed to generate force feedback to reproduce the estimated weight of the object. In the end, comparative experiments show that the proposed scheme can achieve better control accuracy and telepresence, with accurate force feedback generated in only 500 ms.
Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems
Jingyang Mao, Xiangyu Meng, Derui Ding
2022, 9(6): 1026-1036. doi: 10.1109/JAS.2022.105416
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In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.
Distributed Fault-Tolerant Consensus Tracking of Multi-Agent Systems Under Cyber-Attacks
Chun Liu, Bin Jiang, Xiaofan Wang, Huiliao Yang, Shaorong Xie
2022, 9(6): 1037-1048. doi: 10.1109/JAS.2022.105419
Abstract(1168) HTML (329) PDF(167)
This paper investigates the distributed fault-tolerant consensus tracking problem of nonlinear multi-agent systems with general incipient and abrupt time-varying actuator faults under cyber-attacks. First, a decentralized unknown input observer is established to estimate relative states and actuator faults. Second, the estimated and output neighboring information is combined with distributed fault-tolerant consensus tracking controllers. Criteria of reaching leader-following exponential consensus tracking of multi-agent systems under both connectivity-maintained and connectivity-mixed attacks are derived with average dwelling time, attack frequency, and attack activation rate technique, respectively. Simulation example verifies the effectiveness of the fault-tolerant consensus tracking algorithm.
Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity
Mariana Ballesteros, Rita Q. Fuentes-Aguilar, Isaac Chairez
2022, 9(6): 1049-1060. doi: 10.1109/JAS.2022.105650
Abstract(719) HTML (348) PDF(65)
This paper addresses the design of an exponential function-based learning law for artificial neural networks (ANNs) with continuous dynamics. The ANN structure is used to obtain a non-parametric model of systems with uncertainties, which are described by a set of nonlinear ordinary differential equations. Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the ANN. The first algorithm includes an adaptive gain depending on the identification error which accelerated the convergence of the weights and promotes a faster convergence between the states of the uncertain system and the trajectories of the neural identifier. The second approach uses a time-dependent sigmoidal gain that forces the convergence of the identification error to an invariant set characterized by an ellipsoid. The generalized volume of this ellipsoid depends on the upper bounds of uncertainties, perturbations and modeling errors. The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error. Both adaptive algorithms are derived from the application of a non-standard exponential dependent function and an associated controlled Lyapunov function. Numerical examples demonstrate the improvements enforced by the algorithms introduced in this study by comparing the convergence settings concerning classical schemes with non-exponential continuous learning methods. The proposed identifiers overcome the results of the classical identifier achieving a faster convergence to an invariant set of smaller dimensions.
Exploring Image Generation for UAV Change Detection
Xuan Li, Haibin Duan, Yonglin Tian, Fei-Yue Wang
2022, 9(6): 1061-1072. doi: 10.1109/JAS.2022.105629
Abstract(934) HTML (72) PDF(120)
Change detection (CD) is becoming indispensable for unmanned aerial vehicles (UAVs), especially in the domain of water landing, rescue and search. However, even the most advanced models require large amounts of data for model training and testing. Therefore, sufficient labeled images with different imaging conditions are needed. Inspired by computer graphics, we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated dataset. The simulated dataset consists of six challenges to test the effects of dynamic background, weather, and noise on change detection models. Then, we propose an image translation framework that translates simulated images to synthetic images. This framework uses shared parameters (encoder and generator) and 22 × 22 receptive fields (discriminator) to generate realistic synthetic images as model training sets. The experimental results indicate that: 1) different imaging challenges affect the performance of change detection models; 2) compared with simulated images, synthetic images can effectively improve the accuracy of supervised models.
Adaptive Control With Guaranteed Transient Behavior and Zero Steady-State Error for Systems With Time-Varying Parameters
Hefu Ye, Yongduan Song
2022, 9(6): 1073-1082. doi: 10.1109/JAS.2022.105608
Abstract(449) HTML (276) PDF(140)
It is nontrivial to achieve global zero-error regulation for uncertain nonlinear systems. The underlying problem becomes even more challenging if mismatched uncertainties and unknown time-varying control gain are involved, yet certain performance specifications are also pursued. In this work, we present an adaptive control method, which, without the persistent excitation (PE) condition, is able to ensure global zero-error regulation with guaranteed output performance for parametric strict-feedback systems involving fast time-varying parameters in the feedback path and input path. The development of our control scheme benefits from generalized ${\boldsymbol{t}}$-dependent and ${\boldsymbol{x}}$-dependent functions, a novel coordinate transformation and “congelation of variables” method. Both theoretical analysis and numerical simulation verify the effectiveness and benefits of the proposed method.
A Triangulation-Based Visual Localization for Field Robots
James Liang, Yuxing Wang, Yingjie Chen, Baijian Yang, Dongfang Liu
2022, 9(6): 1083-1086. doi: 10.1109/JAS.2022.105632
Abstract(473) HTML (94) PDF(65)
Loop Closure Detection With Reweighting NetVLAD and Local Motion and Structure Consensus
Kaining Zhang, Jiayi Ma, Junjun Jiang
2022, 9(6): 1087-1090. doi: 10.1109/JAS.2022.105635
Abstract(375) HTML (36) PDF(50)
Multiview Locally Linear Embedding for Spectral-Spatial Dimensionality Reduction of Hyperspectral Imagery
Haochen Ji, Zongyu Zuo
2022, 9(6): 1091-1094. doi: 10.1109/JAS.2022.105638
Abstract(323) HTML (84) PDF(55)
A Linear Algorithm for Quantized Event-Triggered Optimization Over Directed Networks
Yang Yuan, Liyu Shi, Wangli He
2022, 9(6): 1095-1098. doi: 10.1109/JAS.2022.105614
Abstract(402) HTML (105) PDF(82)
Attack-Resilient Control Against FDI Attacks in Cyber-Physical Systems
Bo Chen, Yawen Tan, Zhe Sun, Li Yu
2022, 9(6): 1099-1102. doi: 10.1109/JAS.2022.105641
Abstract(438) HTML (54) PDF(100)
Encoding-Decoding-Based Recursive Filtering for Fractional-Order Systems
Bo Jiang, Hongli Dong, Yuxuan Shen, Shujuan Mu
2022, 9(6): 1103-1106. doi: 10.1109/JAS.2022.105644
Abstract(367) HTML (78) PDF(54)
Model Controlled Prediction: A Reciprocal Alternative of Model Predictive Control
Shen Li, Yang Liu, Xiaobo Qu
2022, 9(6): 1107-1110. doi: 10.1109/JAS.2022.105611
Abstract(692) HTML (77) PDF(79)
Part Decomposition and Refinement Network for Human Parsing
Lu Yang, Zhiwei Liu, Tianfei Zhou, Qing Song
2022, 9(6): 1111-1114. doi: 10.1109/JAS.2022.105647
Abstract(549) HTML (56) PDF(64)