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Guaranteed Cost Attitude Tracking Control for Uncertain Quadrotor Unmanned Aerial Vehicle Under Safety Constraints
Qian Ma, Peng Jin, Frank L. Lewis
, Available online  
Abstract:
In this paper, guaranteed cost attitude tracking control for uncertain quadrotor unmanned aerial vehicle (QUAV) under safety constraints is studied. First, an augmented system is constructed by the tracking error system and reference system. This transformation aims to convert the tracking control problem into a stabilization control problem. Then, control barrier function and disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances, and they are incorporated into the reward function as penalty items. Based on the modified reward function, the problem is simplified as the optimal regulation problem of the nominal augmented system, and a new Hamilton-Jacobi-Bellman equation is developed. Finally, critic-only reinforcement learning algorithm with a concurrent learning technique is employed to solve the Hamilton-Jacobi-Bellman equation and obtain the optimal controller. The proposed algorithm can not only ensure the reward function within an upper bound in the presence of uncertain disturbances, but also enforce safety constraints. The performance of the algorithm is evaluated by the numerical simulation.
A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching
Quanbo Ge, Yang Cheng, Hong Li, Ziyi Ye, Yi Zhu, Gang Yao
, Available online  
Abstract:
For accurately identifying the distribution characteristic of Gaussian-like noises in unmanned aerial vehicle (UAV) state estimation, this paper proposes a non-parametric scheme based on curve similarity matching. In the framework of the proposed scheme, a Parzen window (kernel density estimation, KDE) method on sliding window technology is applied for roughly estimating the sample probability density, a precise data probability density function (PDF) model is constructed with the least square method on K-fold cross validation, and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape, abruptness and symmetry. Some comparison simulations with classical methods and UAV flight experiment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data, which provides better reference for the design of Kalman filter (KF) in complex water environment.
Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game
Lei Xue, Jianfeng Ye, Yongbao Wu, Jian Liu, D. C. Wunsch
, Available online  
Abstract:
Partially-Observed Maximum Principle for Backward Stochastic Differential Delay Equations
Shuang Wu
, Available online  
Abstract:
New Second-Level-Discrete Zeroing Neural Network for Solving Dynamic Linear System
Min Yang
, Available online  
Abstract:
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.
Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems
Lihong Feng, Bonan Huang, Jiayue Sun, Qiuye Sun, Xiangpeng Xie
, Available online  
Abstract:
In this paper, a class of time-varying output group formation containment control problem of general linear heterogeneous multiagent systems (MASs) is investigated under directed topology. The MAS is composed of a number of tracking leaders, formation leaders and followers, where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations, respectively. Firstly, compensators are designed whose states are estimations of tracking leaders, based on which, a controller is developed for each formation leader to accomplish the expected formation. Secondly, two event-triggered compensators are proposed for each follower to evaluate the state and formation information of the formation leaders in the same group, respectively. Subsequently, a control protocol is designed for each follower, utilizing the output information, to guide the output towards the convex hull generated by the formation leaders within the group. Next, the triggering sequence in this paper is decomposed into two sequences, and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior. Finally, a numerical simulation is introduced to confirm the validity of the proposed results.
A Novel Prescribed-Performance Path-Following Problem for Non-Holonomic Vehicles
Zirui Chen, Jingchuan Tang, Zongyu Zuo
, Available online  
Abstract:
The issue of achieving prescribed-performance path following in robotics is addressed in this paper, where the aim is to ensure that a desired path within a specified region is accurately converged to by the controlled vehicle. In this context, a novel form of the prescribed performance guiding vector field is introduced, accompanied by a prescribed-time sliding mode control approach. Furthermore, the interdependence among the prescribed parameters is discussed. To validate the effectiveness of the proposed method, numerical simulations are presented to demonstrate the efficacy of the approach.
Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization
Jing Liang, Hongyu Lin, Caitong Yue, Ponnuthurai Nagaratnam Suganthan, Yaonan Wang
, Available online  , doi: 10.1109/JAS.2024.124377
Abstract:
In multimodal multiobjective optimization problems (MMOPs), there are several Pareto optimal solutions corresponding to the identical objective vector. This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables. Due to the increase in the dimensions of decision variables in real-world MMOPs, it is difficult for current multimodal multiobjective optimization evolutionary algorithms (MMOEAs) to find multiple Pareto optimal solutions. The proposed algorithm adopts a dual-population framework and an improved environmental selection method. It utilizes a convergence archive to help the first population improve the quality of solutions. The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population. The combination of these two strategies helps to effectively balance and enhance convergence and diversity performance. In addition, to study the performance of the proposed algorithm, a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed. The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.
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.
Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction
Zhiming Zhang, Shangce Gao, MengChu Zhou, Mengtao Yan, Shuyang Cao
, Available online  
Abstract:
Accurately predicting fluid forces acting on the surface of a structure is crucial in engineering design. However, this task becomes particularly challenging in turbulent flow, due to the complex and irregular changes in the flow field. In this study, we propose a novel deep learning method, named mapping network-coordinated stacked gated recurrent units (MSU), for predicting pressure on a circular cylinder from velocity data. Specifically, our coordinated learning strategy is designed to extract the most critical velocity point for prediction, a process that has not been explored before. In our experiments, MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder. This method significantly reduces the workload of data measurement in practical engineering applications. Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects. Furthermore, the comparison results show that MSU predicts more precise results, even outperforming models that use all velocity field points. Compared with state-of-the-art methods, MSU has an average improvement of more than 45% in various indicators such as root mean square error (RMSE). Through comprehensive and authoritative physical verification, we established that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields. This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios. The code is available at https://github.com/zhangzm0128/MSU.
A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization
Sheng Qi, Rui Wang, Tao Zhang, Xu Yang, Ruiqing Sun, Ling Wang
, Available online  , doi: 10.1109/JAS.2024.124341
Abstract:
Traditional large-scale multi-objective optimization algorithms (LSMOEAs) encounter difficulties when dealing with sparse large-scale multi-objective optimization problems (SLMOPs) where most decision variables are zero. As a result, many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately. Nevertheless, existing optimizers often focus on locating non-zero variable positions to optimize the binary variables Mask. However, approximating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized. In data mining, it is common to mine frequent itemsets appearing together in a dataset to reveal the correlation between data. Inspired by this, we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets (TELSO) to address these SLMOPs. TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence. Experimental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms (SLMOEAs) in terms of performance and convergence speed.
Intuitive Human-Robot-Environment Interaction With EMG Signals: A Review
Dezhen Xiong, Daohui Zhang, Yaqi Chu, Yiwen Zhao, Xingang Zhao
, Available online  , doi: 10.1109/JAS.2024.124329
Abstract:
A long history has passed since electromyography (EMG) signals have been explored in human-centered robots for intuitive interaction. However, it still has a gap between scientific research and real-life applications. Previous studies mainly focused on EMG decoding algorithms, leaving a dynamic relationship between the human, robot, and uncertain environment in real-life scenarios seldomly concerned. To fill this gap, this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interaction (HREI) systems. The general processing framework is summarized, and three interaction paradigms, including direct control, sensory feedback, and partial autonomous control, are introduced. EMG-based intention decoding is treated as a module of the proposed paradigms. Four key issues involving stability, user attention, compliance, and environmental awareness in this field are discussed. Several important directions, including EMG decomposition, robust algorithms, HREI dataset, proprioception feedback, reinforcement learning, and embodied intelligence, are proposed to pave the way for future research. To the best of what we know, this is the first time that a review of EMG-based methods in the HREI system is summarized. It provides a novel and broader perspective to improve the practicability of current myoelectric interaction systems, in which factors in human-robot interaction, robot-environment interaction, and state perception by human sensations are considered, which has never been done by previous studies.
Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey
MengChu Zhou, Meiji Cui, Dian Xu, Shuwei Zhu, Ziyan Zhao, Abdullah Abusorrah
, Available online  , doi: 10.1109/JAS.2024.124320
Abstract:
Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems. The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems (HEPs). The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations. Moreover, it is hard to traverse the huge search space within reasonable resource as problem dimension increases. Traditional evolutionary algorithms (EAs) tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory results. To reduce such evaluations, many novel surrogate-assisted algorithms emerge to cope with HEPs in recent years. Yet there lacks a thorough review of the state of the art in this specific and important area. This paper provides a comprehensive survey of these evolutionary algorithms for HEPs. We start with a brief introduction to the research status and the basic concepts of HEPs. Then, we present surrogate-assisted evolutionary algorithms for HEPs from four main aspects. We also give comparative results of some representative algorithms and application examples. Finally, we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.
Deterministic Learning-Based Neural PID Control for Nonlinear Robotic Systems
Qinchen Yang, Fukai Zhang, Cong Wang
, Available online  , doi: 10.1109/JAS.2024.124224
Abstract:
Traditional proportional-integral-derivative (PID) controllers have achieved widespread success in industrial applications. However, the nonlinearity and uncertainty of practical systems cannot be ignored, even though most of the existing research on PID controllers is focused on linear systems. Therefore, developing a PID controller with learning ability is of great significance for complex nonlinear systems. This article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with uncertainties. The introduction of neural networks (NNs) overcomes the upper limit of the traditional PID feedback mechanism’s capability. The proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional coefficients. Under the partial persistent excitation (PE) condition, the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by NNs. Based on the acquired knowledge from the stable control process, a learning PID controller is developed to further improve overall control performance, while overcoming the problem of repeated online weight updates. Simulation studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.
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.
Recursive Filtering for Stochastic Systems With Filter-and-Forward Successive Relays
Hailong Tan, Bo Shen, Qi Li, Hongjian Liu
, Available online  , doi: 10.1109/JAS.2023.124110
Abstract:
In this paper, the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay (FFSR) networks. An FFSR is located between the sensor and the remote filter to forward the measurement. In the successive relay, two cooperative relay nodes are adopted to forward the signals alternatively, thereby existing switching characteristics and inter-relay interferences (IRI). Since the filter-and-forward scheme is employed, the signal received by the relay is retransmitted after it passes through a linear filter. The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays. First, a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR. Then, novel filter structures with switching parameters are constructed for both FFSR and stochastic systems. With the help of the inductive method, filtering error covariances are presented in the form of coupled difference equations. Next, the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances. Moreover, the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance. Finally, the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.
Nested Saturated Control of Uncertain Complex Cascade Systems Using Mixed Saturation Levels
Meng Li, Zhigang Zeng
, Available online  , doi: 10.1109/JAS.2023.124176
Abstract:
This study addresses the problem of global asymptotic stability for uncertain complex cascade systems composed of multiple integrator systems and non-strict feedforward nonlinear systems. To tackle the complexity inherent in such structures, a novel nested saturated control design is proposed that incorporates both constant saturation levels and state-dependent saturation levels. Specifically, a modified differentiable saturation function is proposed to facilitate the saturation reduction analysis of the uncertain complex cascade systems under the presence of mixed saturation levels. In addition, the design of modified differentiable saturation function will help to construct a hierarchical global convergence strategy to improve the robustness of control design scheme. Through calculation of relevant inequalities, time derivative of boundary surface and simple Lyapunov function, saturation reduction analysis and convergence analysis are carried out, and then a set of explicit parameter conditions are provided to ensure global asymptotic stability in the closed-loop systems. Finally, a simplified system of the mechanical model is presented to validate the effectiveness of the proposed method.
Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis
Jiaxin Ren, Jingcheng Wen, Zhibin Zhao, Ruqiang Yan, Xuefeng Chen, Asoke K. Nandi
, Available online  , doi: 10.1109/JAS.2024.124290
Abstract:
Recently, intelligent fault diagnosis based on deep learning has been extensively investigated, exhibiting state-of-the-art performance. However, the deep learning model is often not truly trusted by users due to the lack of interpretability of “black box”, which limits its deployment in safety-critical applications. A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases, and the human in the decision-making loop can be found to deal with the abnormal situation when the models fail. In this paper, we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks, called SAEU. In SAEU, Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks. Based on the SAEU, we propose a unified uncertainty-aware deep learning framework (UU-DLF) to realize the grand vision of trustworthy fault diagnosis. Moreover, our UU-DLF effectively embodies the idea of “humans in the loop”, which not only allows for manual intervention in abnormal situations of diagnostic models, but also makes corresponding improvements on existing models based on traceability analysis. Finally, two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.
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 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, a differential evolutionary 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 (DE) 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.
A Data-Driven Real-Time Trajectory Planning and Control Methodology for UGVs Using LSTMRDNN
Kaiyuan Chen, Runqi Chai, Runda Zhang, Zhida Xing, Yuanqing Xia, Guoping Liu
, Available online  , doi: 10.1109/JAS.2024.124269
Abstract:
Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach
Tao Chen, Ruiyuan Zhao, Jian Chen, Zichao Zhang
, Available online  , doi: 10.1109/JAS.2023.124158
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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:
Privacy-Preserving Average Consensus Algorithm under Round-Robin Scheduling Protocol
Yingjiang Guo, Wenying Xu, Haodong Wang, Jianquan Lu, Shengli Du
, Available online  , doi: 10.1109/JAS.2023.123921
Abstract:
Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems: Tackling Multiplicative Noises and Missing Measurements
Shaoying Wang, Zidong Wang, Hongli Dong, Yun Chen, Guoping Lu
, Available online  , doi: 10.1109/JAS.2024.124338
Abstract:
This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises, multiple missing measurements as well as the dynamic event-triggered transmission scheme. The multiple missing measurements are characterized through random variables that obey some given probability distributions, and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable. Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense. To this end, the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers, thus the original design issue is reformulated as that of the augmented system. Subsequently, we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters. With the aid of two well-defined matrix difference equations, we not only obtain upper bounds on filtering error covariances, but also minimize those bounds via carefully designing gain parameters. Finally, an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.
Computational Experiments for Complex Social Systems: 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.
Dynamics of the Fractional-Order Lorenz System Based on Adomian Decomposition Method and Its DSP Implementation
Shaobo He, Kehui Sun, Huihai Wang
, Available online  
Abstract:
Output Feedback Stabilization of High-Order Nonlinear Time-Delay Systems With Low-Order and High-Order Nonlinearities
Meng-Meng Jiang, Kemei Zhang, Xue-Jun Xie
, Available online  
Abstract:
Passivity-Based Stabilization for Switched Stochastic Nonlinear Systems
Yaowei Sun, Jun Zhao
, Available online  
Abstract:
Event-Triggered Bipartite Consensus Tracking and Vibration Control of Flexible Timoshenko Manipulators Under Time-Varying Actuator Faults
Xiangqian Yao, Hao Sun, Zhijia Zhao, Yu Liu
, Available online  , doi: 10.1109/JAS.2024.124266
Abstract:
For bipartite angle consensus tracking and vibration suppression of multiple Timoshenko manipulator systems with time-varying actuator faults, parameter and modeling uncertainties, and unknown disturbances, a novel distributed boundary event-triggered control strategy is proposed in this work. In contrast to the earlier findings, time-varying consensus tracking and actuator defects are taken into account simultaneously. In addition, the constructed event-triggered control mechanism can achieve a more flexible design because it is not required to satisfy the input-to-state condition. To achieve the control objectives, some new integral control variables are given by using back-stepping technique and boundary control. Moreover, adaptive neural networks are applied to estimate system uncertainties. With the proposed event-triggered scheme, control inputs can reduce unnecessary updates. Besides, tracking errors and vibration states of the closed-looped network can be exponentially convergent into some small fields, and Zeno behaviors can be excluded. At last, some simulation examples are given to state the effectiveness of the control algorithms.
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.
Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation
Ke Li, Shunyi Zhao, Biao Huang, Fei Liu
, Available online  
Abstract:
In the era of exponential growth of data availability, the architecture of systems has a trend toward high dimensionality, and directly exploiting holistic information for state inference is not always computationally affordable. This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems. The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions. After that, two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space, mitigating the performance degradation caused by state segmentation. Moreover, the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods. Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.
Prescribed Performance Evolution Control for Quadrotor Autonomous Shipboard Landing
Yang Yuan, Haibin Duan, Zhigang Zeng
, Available online  
Abstract:
The shipboard landing problem for a quadrotor is addressed in this paper, where the ship trajectory tracking control issue is transformed into a stabilization control issue by building a relative position model. To guarantee both transient performance and steady-state landing error, a prescribed performance evolution control (PPEC) method is developed for the relative position control. In addition, a novel compensation system is proposed to expand the performance boundaries when the input saturation occurs and the error exceeds the predefined threshold. Considering the wind and wave on the relative position model, an adaptive sliding mode observer (ASMO) is designed for the disturbance with unknown upper bound. Based on the dynamic surface control framework, a shipboard landing controller integrating PPEC and ASMO is established for the quadrotor, and the relative position control error is guaranteed to be uniformly ultimately bounded. Simulation results have verified the feasibility and effectiveness of the proposed shipboard landing control scheme.
Multi-Axis Attention With Convolution Parallel Block for Organoid Segmentation
Pengwei Hu, Xun Deng, Feng Tan, Lun Hu
, Available online  
Abstract:
State-Based Opacity Verification of Networked Discrete Event Systems Using Labeled Petri Nets
Yifan Dong, Naiqi Wu, Zhiwu Li
, Available online  
Abstract:
The opaque property plays an important role in the operation of a security-critical system, implying that pre-defined secret information of the system is not able to be inferred through partially observing its behavior. This paper addresses the verification of current-state, initial-state, infinite-step, and K-step opacity of networked discrete event systems modeled by labeled Petri nets, where communication losses and delays are considered. Based on the symbolic technique for the representation of states in Petri nets, an observer and an estimator are designed for the verification of current-state and initial-state opacity, respectively. Then, we propose a structure called an I-observer that is combined with secret states to verify whether a networked discrete event system is infinite-step opaque or K-step opaque. Due to the utilization of symbolic approaches for the state-based opacity verification, the computation of the reachability graphs of labeled Petri nets is avoided, which dramatically reduces the computational overheads stemming from networked discrete event systems.
Fixed-Time Cluster Optimization for Multi-Agent Systems Based on Piecewise Power-Law Design
Suna Duan, Xinchun Jia, Xiaobo Chi
, Available online  
Abstract:
Input-to-State Stability of Impulsive Switched Systems Involving Uncertain Impulse-switching Moments
Chang Liu, Wenlu Liu, Tengda Wei, Xiaodi Li
, Available online  
Abstract:
A Local-Global Attention Fusion Framework With Tensor Decomposition for Medical Diagnosis
Peishu Wu, Han Li, Liwei Hu, Jirong Ge, Nianyin Zeng
, Available online  
Abstract:
Stabilization Controller of An Extended Chained Nonholonomic System With Disturbance: An FAS Approach
Zhongcai Zhang, Guangren Duan
, Available online  , doi: 10.1109/JAS.2023.124098
Abstract:
This study examines the stabilization issue of extended chained nonholonomic systems (ECNSs) with external disturbance. Unlike existing approaches, we transform the considered system into a fully actuated system (FAS) model, simplifying the stabilizing controller design. We implement a separate controller design and propose exponential stabilization controller and finite-time stabilization controllers under finite-time disturbance observer (FTDO) for the two system inputs. In addition, we discuss the specifics of global stabilization control design. Our approach demonstrates that two system states exponentially or asymptotically converge to zero under the provided switching stabilization control strategy, while all other system states converge to zero within a finite time.
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.
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.
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:
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: