Early Access

Display Method:
Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyla-denosine Site Identification
Guodong Li, Bowei Zhao, Xiaorui Su, Dongxu Li, Yue Yang, Zhi Zeng, Lun Hu
, Available online  
Abstract:
N6-methyladenosine (m6A) is an important RNA methylation modification involved in regulating diverse biological processes across multiple species. Hence, the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level. Although a variety of identification algorithms have been proposed recently, most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences, while ignoring the structural dependencies of nucleotides in their three-dimensional structures. To overcome this issue, we propose a cross-species end-to-end deep learning model, namely CR-NSSD, which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification. Specifically, CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory. It then constructs a cross-domain reconstruction encoder to learn the sequential and structural dependencies between nucleotides. By minimizing the reconstruction and binary cross-entropy losses, CR-NSSD is trained to complete the task of m6A site identification. Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms. Moreover, the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species, thus improving the accuracy of cross-species identification.
High-Order Control Barrier Function-Based Safety Control of Constrained Robotic Systems: An Augmented Dynamics Approach
Haijing Wang, Jinzhu Peng, Fangfang Zhang, Yaonan Wang
, Available online  , doi: 10.1109/JAS.2024.124524
Abstract:
Although constraint satisfaction approaches have achieved fruitful results, system states may lose their smoothness and there may be undesired chattering of control inputs due to switching characteristics. Furthermore, it remains a challenge when there are additional constraints on control torques of robotic systems. In this article, we propose a novel high-order control barrier function (HoCBF)-based safety control method for robotic systems subject to input-output constraints, which can maintain the desired smoothness of system states and reduce undesired chattering vibration in the control torque. In our design, augmented dynamics are introduced into the HoCBF by constructing its output as the control input of the robotic system, so that the constraint satisfaction is facilitated by HoCBFs and the smoothness of system states is maintained by the augmented dynamics. This proposed scheme leads to the quadratic program (QP), which is more user-friendly in implementation since the constraint satisfaction control design is implemented as an add-on to an existing tracking control law. The proposed closed-loop control system not only achieves the requirements of real-time capability, stability, safety and compliance, but also reduces undesired chattering of control inputs. Finally, the effectiveness of the proposed control scheme is verified by simulations and experiments on robotic manipulators.
Neural Network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol
Xueli Wang, Shangwei Zhao, Ming Yang, Xin Wang, Xiaoming Wu
, Available online  
Abstract:
A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning
Yi Liu, Xiang Wu, Yuming Bo, Jiacun Wang, Lifeng Ma
, Available online  , doi: 10.1109/JAS.2023.124173
Abstract:
Distributed Predefined-Time Control for Cooperative Tracking of Multiple Quadrotor UAVs
Kewei Xia, Xinyi Li, Kaidan Li, Yao Zou
, Available online  , doi: 10.1109/JAS.2023.123861
Abstract:
Data Driven Vibration Control: A Review
Weiyi Yang, Shuai Li, Xin Luo
, Available online  
Abstract:
With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing, aviation, and healthcare, the data driven vibration control (DDVC) has attracted broad interests from both the industrial and academic communities. Input shaping (IS), as a simple and effective feedforward method, is greatly demanded in DDVC methods. It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure, thereby suppressing the residual vibration separately. Based on a thorough investigation into the state-of-the-art DDVC methods, this survey has made the following efforts: 1) Introducing the IS theory and typical input shapers; 2) Categorizing recent progress of DDVC methods; 3) Summarizing commonly adopted metrics for DDVC; and 4) Discussing the engineering applications and future trends of DDVC. By doing so, this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives, aiming at promoting future research regarding this emerging and vital issue.
A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry
Xuerui Zhang, Zhongyang Han, Jun Zhao
, Available online  , doi: 10.1109/JAS.2023.124116
Abstract:
Ingredient optimization plays a pivotal role in the copper industry, for which it is closely related to the concentrate utilization rate, stability of furnace conditions, and the quality of copper production. To acquire a practical ingredient plan, which should exhibit long duration time with sufficient utilization and feeding stability for real applications, an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace conditions. To address the complex challenges posed by this integer programming model, including multiple coupling feeding stages, intricate constraints, and significant non-linearity, a multi-stage differential-multifactorial evolution algorithm is developed. In the proposed algorithm, the differential evolutionary (DE) algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed model. First, unlike traditional time-consuming serial approaches, the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm caused by the feeding stability in a parallel manner. Second, a repair algorithm is employed to adjust infeasible ingredient lists in a timely manner. In addition, a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary algorithm. Finally, the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted, which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed approaches. It is practically helpful for reducing material cost as well as increasing production profit for the copper industry.
Achieving Given Precision Within Prescribed Time yet With Guaranteed Transient Behavior via Output Based Event-Triggered Control
Zeqiang Li, Yujuan Wang, Yongduan Song
, Available online  , doi: 10.1109/JAS.2023.124134
Abstract:
It is interesting yet nontrivial to achieve given control precision within user-assignable time for uncertain nonlinear systems. The underlying problem becomes even more challenging if the transient behavior also needs to be accommodated and only system output is available for feedback. Several key design innovations are proposed to circumvent the aforementioned technical difficulties, including the employment of state estimation filters with event-triggered mechanism, the construction of a novel performance scaling function and an error transformation. In contrast to most existing performance based works where the stability is contingent on initial conditions and the maximum allowable steady-state tracking precision can only be guaranteed at some unknown (theoretically infinite) time, in this work the output of the system is ensured to synchronize with the desired trajectory with arbitrarily pre-assignable convergence rate and arbitrarily pre-specified precision within prescribed time, using output only with lower cost of sensing and communication. In addition, all the closed-loop signals are ensured to be globally uniformly bounded under the proposed control method. The merits of the designed control scheme are confirmed by numerical simulation on a ship model.
Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition
Shouyong Jiang, Jinglei Guo, Yong Wang, Shengxiang Yang
, Available online  
Abstract:
Decomposition of a complex multi-objective optimisation problem (MOP) to multiple simple subMOPs, known as M2M for short, is an effective approach to multi-objective optimisation. However, M2M facilitates little communication/collaboration between subMOPs, which limits its use in complex optimisation scenarios. This paper extends the M2M framework to develop a unified algorithm for both multi-objective and many-objective optimisation. Through bilevel decomposition, an MOP is divided into multiple subMOPs at upper level, each of which is further divided into a number of single-objective subproblems at lower level. Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another, and eventually to all the subMOPs. The bilevel decomposition is readily combined with some new mating selection and population update strategies, leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multi- and many-objective optimisation. Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.
Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control
Yisha Li, Ya Zhang, Xinde Li, Changyin Sun
, Available online  
Abstract:
This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system. A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency. Firstly a regional multi-agent Q-learning framework is proposed, which can equivalently decompose the global Q value of the traffic system into the local values of several regions. Based on the framework and the idea of human-machine cooperation, a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to real-time traffic flow densities. In order to achieve better cooperation inside each region, a lightweight spatio-temporal fusion feature extraction network is designed. The experiments in synthetic, real-world and city-level scenarios show that the proposed RegionSTLight converges more quickly, is more stable, and obtains better asymptotic performance compared to state-of-the-art models.
Accumulative-Error-Based Event-Triggered Control for Discrete-Time Linear Systems: A Discrete-Time Looped Functional Method
Xian-Ming Zhang, Qing-Long Han, Xiaohua Ge, Bao-Lin Zhang
, Available online  , doi: 10.1109/JAS.2024.124476
Abstract:
This paper is concerned with event-triggered control of discrete-time systems with or without input saturation. First, an accumulative-error-based event-triggered scheme is devised for control updates. When the accumulated error between the current state and the latest control update exceeds a certain threshold, an event is triggered. Such a scheme can ensure the event-generator works at a relatively low rate rather than falls into hibernation especially after the system steps into its steady state. Second, the looped functional method for continuous-time systems is extended to discrete-time systems. By introducing an innovative looped functional that links the event-triggered scheme, some sufficient conditions for the co-design of control gain and event-triggered parameters are obtained in terms of linear matrix inequalities with a couple of tuning parameters. Then, the proposed method is applied to discrete-time systems with input saturation. As a result, both suitable control gains and event-triggered parameters are also co-designed to ensure the system trajectories converge to the region of attraction. Finally, an unstable reactor system and an inverted pendulum system are given to show the effectiveness of the proposed method.
Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production
Kailong Liu, Qiao Peng, Yuhang Liu, Naxin Cui, Chenghui Zhang
, Available online  , doi: 10.1109/JAS.2024.124539
Abstract:
Battery production is crucial for determining the quality of electrode, which in turn affects the manufactured battery performance. As battery production is complicated with strongly coupled intermediate and control parameters, an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required. This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling. To be specific, an explainable neural network named GAM-SI (generalized additive model with structured interaction) is designed to predict two key battery properties, including electrode mass loading and porosity, while the effects of four early production terms on manufactured batteries are explained and analyzed. The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages. In addition, the importance ratio ranking, global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network. Due to the merits of interpretability, the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior, further benefitting smart battery production.
Target Controllability of Multi-Layer Networks With High-Dimensional Nodes
Lifu Wang, Zhaofei Li, Ge Guo, Zhi Kong
, Available online  
Abstract:
This paper studies the target controllability of multi-layer complex networked systems, in which the nodes are high-dimensional linear time invariant (LTI) dynamical systems, and the network topology is directed and weighted. The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed. It is found that even if there exists a layer which is not target controllable, the entire multi-layer network can still be target controllable due to the inter-layer couplings. For the multi-layer networks with general structure, a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix. By the derived condition, it can be found that the system may be target controllable even if it is not state controllable. On this basis, two corollaries are derived, which clarify the relationship between target controllability, state controllability and output controllability. For the multi-layer networks where the inter-layer couplings are directed chains and directed stars, sufficient conditions for target controllability of networked systems are given, respectively. These conditions are easier to verify than the classic criterion.
Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation
Xiaolong Chen, Biao Xu, Manjiang Hu, Yougang Bian, Yang Li, Xin Xu
, Available online  , doi: 10.1109/JAS.2024.124287
Abstract:
Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently. This paper proposes a reinforcement learning (RL) method for autonomous vehicles to navigate unsignalized intersections safely and efficiently. The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning. A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them. A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections. The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem. The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency. Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions. The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.
RMPC-based Visual Servoing for Trajectory Tracking of Quadrotor UAVs With Visibility Constraints
Qifan Yang, Huiping Li
, Available online  
Abstract:
Scalable Temporal Dimension Preserved Tensor Completion for Missing Traffic Data Imputation With Orthogonal Initialization
Hong Chen, Mingwei Lin, Jiaqi Liu, Zeshui Xu
, Available online  , doi: 10.1109/JAS.2024.124278
Abstract:
A Distortion Self-Calibration Method for Binocular High Dynamic Light Adjusting and Imaging System Based on Digital Micromirror Device
Pei Wu, Yanjie Wang, Honghai Sun, Zhuoman Wen
, Available online  
Abstract:
A Novel Approach for Trajectory Tracking Control of an Under-Actuated Quad-Rotor UAV
Ke Shao, Kang Huang, Shengchao Zhen, Hao Sun, Rongrong Yu
, Available online  
Abstract:
Distributed Optimal Formation Control of Unmanned Aerial Vehicles: Theory and Experiments
Gang Wang, Zhenhong Wei, Peng Li
, Available online  , doi: 10.1109/JAS.2024.124518
Abstract:
Multi-Interval-Aggregation Failure Point Approximation for Remaining Useful Life Prediction
Linchuan Fan, Xiaolong Chen, Shuo Li, Yi Chai
, Available online  , doi: 10.1109/JAS.2024.124593
Abstract:
Fuzzy-Model-Based Finite Frequency Fault Detection Filtering Design for Two-Dimensional Nonlinear Systems
Meng Wang, Huaicheng Yan, Jianbin Qiu, Wenqiang Ji
, Available online  , doi: 10.1109/JAS.2024.124452
Abstract:
This article studies the fault detection filtering design problem for Roesser type two-dimensional (2-D) nonlinear systems described by uncertain 2-D Takagi-Sugeno (T-S) fuzzy models. Firstly, fuzzy Lyapunov functions are constructed and the 2-D Fourier transform is exploited, based on which a finite frequency fault detection filtering design method is proposed such that a residual signal is generated with robustness to external disturbances and sensitivity to faults. It has been shown that the utilization of available frequency spectrum information of faults and disturbances makes the proposed filtering design method more general and less conservative compared with a conventional non-frequency based filtering design approach. Then, with the proposed evaluation function and its threshold, a novel mixed finite frequency $ {\cal{H}}_{\infty}/{\cal{H}}_{-}$ fault detection algorithm is developed, based on which the fault can be immediately detected once the evaluation function exceeds the threshold. Finally, it is verified with simulation studies that the proposed method is effective and less conservative than conventional non-frequency and/or common Lyapunov function based filtering design methods.
Optimal Secure Control of Networked Control Systems Under False Data Injection Attacks: A Multi-Stage Attack-Defense Game Approach
Dajun Du, Yi Zhang, Baoyue Xu, Minrui Fei
, Available online  , doi: 10.1109/JAS.2023.124005
Abstract:
Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems
Babak Esmaeili, Hamidreza Modares
, Available online  , doi: 10.1109/JAS.2024.124479
Abstract:
This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems. The nonlinear system is modeled using linear parameter-varying (LPV) systems. A model-based probabilistic safe controller is first designed to guarantee probabilistic $\lambda$-contractivity (i.e., stability and invariance) of the LPV system with respect to a given polyhedral safe set. To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model, its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable. It is shown that the variance of the closed-loop system, as well as the probability of safety satisfaction, depends on the decision variable and the noise covariance. A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level. This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set, and thus minimizes the risk of safety violation. Unlike the certainty-equivalent approach that results in a risk-neutral control design, the minimum-variance method leads to a risk-averse control design. It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation. Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.
A Generalized Array Factor for Time-Modulated Hexagonal Based Antenna Array Geometry With Novel Trapezoidal Switching
Gopi Ram
, Available online  , doi: 10.1109/JAS.2024.124458
Abstract:
The concept of the time-modulated array has been emerging as an alternative to the complex phase shifters, which lowers the cost of the array feeding network due to the utilization of radio frequency (RF) switches. The various forms of hexagonal antenna array geometries can be used for applications like surveillance tracking in phased array radar and wireless communication systems. This work proposes the generalized array factor (AF) for the hexagonal antenna array geometry based on time modulation. The time modulation in generalized hexagonal geometry can maintain the fixed static amplitude excitation, giving more flexibility over time. Furthermore, a novel trapezoidal switching function is also proposed and applied to the generalized array factor to enable future researchers to use this array factor in the field of advancement to observe how switching schemes like trapezoidal and rectangular affect the array pattern’s side lobe level (SLL). The generalized equation can be utilized for the analysis and synthesis of radiation characteristics of the time-modulated hexagonal array (TMHA), time-modulated concentric hexagonal array (TMCHA), time-modulated hexagonal cylindrical array (TMHCA), and time-modulated hexagonal concentric cylindrical array (TMHCCA). The numerical result illustrates the generation of AF of time-modulated hexagonal structures and also shows that the trapezoidal switching sequence outperforms the rectangular switch using the cat swarm optimization (CSO) approach.
Non-Singular Practical Fixed-time Prescribed Performance Adaptive Fuzzy Consensus Control for Multi-Agent Systems Based on an Observer
Chi Ma, Dianbiao Dong
, Available online  , doi: 10.1109/JAS.2024.124428
Abstract:
In this paper, the problem of non-singular fixed-time control with prescribed performance is studied for multi-agent systems characterized by uncertain states, nonlinearities, and non-strict feedback. To mitigate the nonlinearity, a fuzzy logic algorithm is applied to approximate the intrinsic dynamics of the system. Furthermore, a fuzzy logic system state observer based on leader state information is designed to address the partial unobservability of followers. Subsequently, the power integral method is incorporated into the backstepping approach to avoid singularities in the fixed-time controller. A command filter method is introduced into the standard backstepping approach to reduce the computational complexity of controller design. Then, a non-singular fixed-time adaptive control strategy with prescribed performance is proposed by constraining the tracking error within a prescribed range. Rigorous theoretical analysis ensures the convergence of consensus error in the multi-agent system to the prescribed performance region within a fixed time. Finally, the practicality of the algorithm is validated through numerical simulations.
Distributed Finite-Time Formation Control of Multiple Mobile Robot Systems Without Global Information
Xunhong Sun, Haibo Du, Weile Chen, Wenwu Zhu
, Available online  , doi: 10.1109/JAS.2023.123981
Abstract:
Safety-Critical Trajectory Tracking for Mobile Robots With Guaranteed Performance
Wentao Wu, Di Wu, Yibo Zhang, Shukang Chen, Weidong Zhang
, Available online  , doi: 10.1109/JAS.2023.123864
Abstract:
A Distributed Adaptive Second-Order Latent Factor Analysis Model
Jialiang Wang, Weiling Li, Xin Luo
, Available online  , doi: 10.1109/JAS.2024.124371
Abstract:
Semi-Decentralized Convex Optimization on \begin{document}$ {\cal{SO}}(3)$\end{document}
Weijian Li, Peng Yi
, Available online  , doi: 10.1109/JAS.2024.124356
Abstract:
New Controllability Criteria for Linear Switched and Impulsive Systems
Jiayuan Yan, Bin Hu, Zhi-Hong Guan, Yandong Hou, Lei Shi
, Available online  , doi: 10.1109/JAS.2024.124272
Abstract:
Cognitive Navigation for Intelligent Mobile Robots: A Learning-Based Approach With Topological Memory Configuration
Qiming Liu, Xinru Cui, Zhe Liu, Hesheng Wang
, Available online  , doi: 10.1109/JAS.2024.124332
Abstract:
Autonomous navigation for intelligent mobile robots has gained significant attention, with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory. In this paper, we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations. We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation. This tackles the issues of topological node redundancy and incorrect edge connections, which stem from the distribution gap between the spatial and perceptual domains. Furthermore, we propose a differentiable graph extraction structure, the topology multi-factor transformer (TMFT). This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation. Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures. Comprehensive validation through behavior visualization, interpretability tests, and real-world deployment further underscore the adaptability and efficacy of our method.
Set-Valued State Estimation of Nonlinear Discrete-Time Systems and Its Application to Attack Detection
Hao Liu, Qing-Long Han, Yuzhe Li
, Available online  
Abstract:
This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded (UBB) noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets. First, properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity. Then, the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets. Based on generalized intersection, the utilization of set-based estimation for attack detection is analyzed. Finally, an example is given to show the efficiency of our results.
A Novel Vibration-Based Self-Adapting Method to Acquire Real-Time Following Distance for Virtually Coupled Trains
Qinglai Zhang, Jianmin Gao, Qing Wu, Qinglie He, Libin Tie, Wanming Zhai, Shengyang Zhu
, Available online  , doi: 10.1109/JAS.2024.124326
Abstract:
Virtual coupling (VC) is an emerging technology for addressing the shortage of rail transportation capacity. As a crucial enabling technology, the VC-specific acquisition of train information, especially train following distance (TFD), is underdeveloped. In this paper, a novel method is proposed to acquire real-time TFD by analyzing the vibration response of the front and following trains, during which only onboard accelerometers and speedometers are required. In contrast to the traditional arts of train positioning, this method targets a relative position between two adjacent trains in VC operation, rather than the global positions of the trains. For this purpose, an adaptive system containing three strategies is designed to cope with possible adverse factors in train operation. A vehicle dynamics simulation of a heavy-haul railway is implemented for the evaluation of feasibility and performance. Furthermore, a validation is conducted using a set of data measured from in-service Chinese high-speed trains. The results indicate the method achieves satisfactory estimation accuracy using both simulated and actual data. It has favorable adaptability to various uncertainties possibly encountered in train operation. Additionally, the method is preliminarily proven to adapt to different locomotive types and even different rail transportation modes. In general, such a method with good performance, low-cost, and easy implementation is promising to apply.
Event-Based Networked Predictive Control of Cyber-Physical Systems With Delays and DoS Attacks
Wencheng Luo, Pingli Lu, Changkun Du, Haikuo Liu
, Available online  
Abstract:
A Multi-Constrained Matrix Factorization Approach for Community Detection Relying on Alternating-Direction-Method of Multipliers
Ying Shi, Zhigang Liu
, Available online  
Abstract:
A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs
Yalin Zhang, Zhongxin Liu, Zengqiang Chen
, Available online  , doi: 10.1109/JAS.2024.124236
Abstract:
Battery energy storage systems (BESSs) are widely used in smart grids. However, power consumed by inner impedances and the capacity degradation of each battery unit become particularly severe, which has resulted in an increase in operating costs. The general economic dispatch (ED) algorithm based on marginal cost (MC) consensus is usually a proportional (P) controller, which encounters the defects of slow convergence speed and low control accuracy. In order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance, we attempt to design a proportional integral (PI) controller with a reset mechanism (PI+R) to asymptotically promote MC consensus and total power mismatch towards 0 in this paper. To be frank, the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing, which accelerates convergence, improves control accuracy, and avoids overshoot. The eigenvalues of the system under a PI+R controller is well analyzed, ensuring the regularity of the system and enabling the reset mechanism. To ensure supply and demand balance within the isolated BESSs, a centralized reset mechanism is introduced, so that the controller is distributed in a flow set and centralized in a jump set. To cope with Zeno behavior and input delay, a dwell time that the system resides in a flow set is given. Based on this, the system with input delays can be reduced to a time-delay free system. Considering the capacity limitation of the battery, a modified MC scheme with PI+R controller is designed. The correctness of the designed scheme is verified through relevant simulations.
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:
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:
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:
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:
Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
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: