IEEE/CAA Journal of Automatica Sinica

GPIO-Based Continuous Sliding Mode Control for Networked Control Systems Under Communication Delays With Experiments on Servo Motors

Kamal Rsetam, Zhenwei Cao, Zhihong Man, Xian-Ming Zhang

, Available online , doi: 10.1109/JAS.2024.124812

[Abstract](51) [FullText HTML] (0) [PDF 6049KB](16)

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To handle input and output time delays that commonly exist in many networked control systems (NCSs), a new robust continuous sliding mode control (CSMC) scheme is proposed for the output tracking in uncertain single input-single-output (SISO) networked control systems. This scheme consists of three consecutive steps. First, although the network-induced delay in those systems can be effectively handled by using Pade approximation (PA), the unmatched disturbance cames out as another difficulty in the control design. Second, to actively estimate this unmatched disturbance, a generalized proportional integral observer (GPIO) technique is utilized based on only one measured state. Third, by constructing a new sliding manifold with the aid of the estimated unmatched disturbance and states, a GPIO-based CSMC is synthesized, which is employed to cope with not only matched and unmatched disturbances, but also network-induced delays. The stability of the entire closed-loop system under the proposed GPIO-based CSMC is detailedly analyzed. The promising tracking efficiency and feasibility of the proposed control methodology are verified through simulations and experiments on Quanser’s servo module for motion control under various test conditions.

K-Corruption Intermittent Attacks for Violating the Codiagnosability

Ruotian Liu, Yihui Hu, Agostino Marcello Mangini, Maria Pia Fanti

, Available online , doi: 10.1109/JAS.2024.124680

[Abstract](14) [FullText HTML] (0) [PDF 2024KB](3)

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In this work, we address the codiagnosability analysis problem of a networked discrete event system under malicious attacks. The considered system is modeled by a labeled Petri net and is monitored by a series of sites, in which each site possesses its own set of sensors, without requiring communication among sites or to any coordinators. A net is said to be codiagnosable with respect to a fault if at least one site could deduce the occurrence of this fault within finite steps. In this context, we focus on a type of malicious attack that is called stealthy intermittent replacement attack. The stealthiness demands that the corrupted observations should be consistent with the system’s normal behavior, while the intermittent replacement setting entails that the replaced transition labels must be recovered within a bounded of consecutive corrupted observations (called as K-corruption intermittent attack). Particularly, there exists a coordination between attackers that are separately effected on different sites, which holds the same corrupted observation for each common transition under attacks. From an attacker viewpoint, this work aims to design K-corruption intermittent attacks for violating the codiagnosability of systems. For this purpose, we propose an attack automaton to analyze K-corruption intermittent attack for each site, and build a new structure called complete attack graph that is used to analyze all the potential attacked paths. Finally, an algorithm is inferred to obtain the K-corruption intermittent attacks, and examples are given to show the proposed attack strategy.

Controllability of Multi-Relational Networks With Heterogeneous Dynamical Nodes

Lifu Wang, Zhaofei Li, Lianqian Cao, Ge Guo, Zhi Kong

, Available online

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This paper studies the controllability of networked systems, in which the nodes are heterogeneous high-dimensional dynamical systems, and the links between nodes are multi-relational. Our aim is to find controllability criteria for heterogeneous networks with multi-relational links beyond those only applicable to networks with single-relational links. It is found a network with multi-relational links can be controllable even if each single-relational network topology is uncontrollable, and vice versa. Some sufficient and necessary conditions are derived for the controllability of multi-relational networks with heterogeneous dynamical nodes. For two typical multi-relational networks with star-chain topology and star-circle topology, some easily verified conditions are presented. For illustration and verification, several examples are presented. These findings provide practical insights for the analysis and control of multi-relational complex systems.

Privacy Preserving Distributed Bandit Residual Feedback Online Optimization Over Time-Varying Unbalanced Graphs

Zhongyuan Zhao, Zhiqiang Yang, Luyao Jiang, Ju Yang, Quanbo Ge

, Available online , doi: 10.1109/JAS.2024.124656

[Abstract](34) [FullText HTML] (0) [PDF 1332KB](11)

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This paper considers the distributed online optimization (DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed online one-point residual feedback (OPRF) optimization algorithm is proposed. This algorithm updates decision variables by leveraging one-point residual feedback to estimate the true gradient information. It can achieve the same performance as the two-point feedback scheme while only requiring a single function value query per iteration. Additionally, it effectively eliminates the effect of time-varying unbalanced graphs by dynamically constructing row stochastic matrices. Furthermore, compared to other distributed optimization algorithms that only consider explicitly unknown cost functions, this paper also addresses the issue of privacy information leakage of nodes. Theoretical analysis demonstrate that the method attains sublinear regret while protecting the privacy information of agents. Finally, numerical experiments on distributed collaborative localization problem and federated learning confirm the effectiveness of the algorithm.

Distributed Economic Dispatch Algorithms of Microgrids Integrating Grid-Connected and Isolated Modes

Zhongxin Liu, Yanmeng Zhang, Yalin Zhang, Fuyong Wang

, Available online

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The economic dispatch problem (EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size, the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching. Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations.

Robust Pose Graph Optimization Against Outliers Using Consistency Credibility Factor

Jie Cai, Guoliang Wei, Wangyan Li, Yaolei Wang

, Available online , doi: 10.1109/JAS.2023.123897

[Abstract](13) [FullText HTML] (0) [PDF 513KB](0)

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Cas-FNE: Cascaded Face Normal Estimation

Meng Wang, Jiawan Zhang, Jiayi Ma, Xiaojie Guo

, Available online

[Abstract](9) [FullText HTML] (0) [PDF 41212KB](3)

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Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graphics applications. Though significant progress has been made in recent years, how to effectively and efficiently explore normal priors remains challenging. Most existing approaches depend on the development of intricate network architectures and complex calculations for in-the-wild face images. To overcome the above issue, we propose a simple yet effective cascaded neural network, called Cas-FNE, which progressively boosts the quality of predicted normals with marginal model parameters and computational cost. Meanwhile, it can mitigate the imbalance issue between training data and real-world face images due to the progressive refinement mechanism, and thus boost the generalization ability of the model. Specifically, in the training phase, our model relies solely on a small amount of labeled data. The earlier prediction serves as guidance for following refinement. In addition, our shared-parameter cascaded block employs a recurrent mechanism, allowing it to be applied multiple times for optimization without increasing network parameters. Quantitative and qualitative evaluations on benchmark datasets are conducted to show that our Cas-FNE can faithfully maintain facial details and reveal its superiority over state-of-the-art methods. The code is available at https://github.com/AutoHDR/CasFNE.git.

Distributed Fixed-Time Optimal Energy Management for Microgrids Based on a Dynamic Event-Triggered Mechanism

Feisheng Yang, Jiaming Liu, Xiaohong Guan

, Available online

[Abstract](15) [FullText HTML] (0) [PDF 1932KB](7)

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The article investigates the optimal energy management (OEM) problem for microgrids. To figure out the OEM problem in fixed time and alleviate communication load with limited resources, this article devises a novel fixed-time stability lemma and a dynamic event-triggered (ET) fixed-time distributed OEM approach. Using Lyapunov stability theory, the distributed approach has been proven to converge in fixed time and the upper bound on convergence time can be derived without dependence on the initial states. The dynamic ET method is raised to dynamically adjust the triggering threshold and reduce communication redundancy. In addition, Zeno behavior is avoided. Simulations are given to show the effectiveness and advantage of the designed distributed OEM method.

A Game-Theoretic Approach to Solving the Roman Domination Problem

Xiuyang Chen, Changbing Tang, Zhao Zhang, Guanrong Chen

, Available online , doi: 10.1109/JAS.2023.123840

[Abstract](22) [FullText HTML] (0) [PDF 2230KB](5)

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The Roman domination problem is an important combinatorial optimization problem that is derived from an old story of defending the Roman Empire and now regains new significance in cyber space security, considering backups in the face of a dynamic network security requirement. In this paper, firstly, we propose a Roman domination game (RDG) and prove that every Nash equilibrium (NE) of the game corresponds to a strong minimal Roman dominating function (S-RDF), as well as a Pareto-optimal solution. Secondly, we show that RDG is an exact potential game, which guarantees the existence of an NE. Thirdly, we design a game-based synchronous algorithm (GSA), which can be implemented distributively and converge to an NE in $ O(n)$ rounds, where n is the number of vertices. In GSA, all players make decisions depending on local information. Furthermore, we enhance GSA to be enhanced GSA (EGSA), which converges to a better NE in $ O(n^2)$ rounds. Finally, we present numerical simulations to demonstrate that EGSA can obtain a better approximate solution in promising computation time compared with state-of-the-art algorithms.

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](94) [FullText HTML] (0) [PDF 13298KB](38)

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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.

Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

Ying Chen, Feilong Lin, Zhongyu Chen, Changbing Tang, Cailian Chen

, Available online , doi: 10.1109/JAS.2024.124626

[Abstract](7) [FullText HTML] (0) [PDF 1850KB](3)

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The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchain-based peer-to-peer (P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users’ privacy via blockchain-based smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable, and achieves 90% fairness of enterprises.

Disturbance Rejection for Systems With Uncertainties Based on Fixed-Time Equivalent-Input-Disturbance Approach

Qun Lu, Xiang Wu, Jinhua She, Fanghong Guo, Li Yu

, Available online , doi: 10.1109/JAS.2024.124650

[Abstract](136) [FullText HTML] (0) [PDF 2577KB](41)

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This paper presents a fixed-time equivalent-input-disturbance (EID) approach to deal with the problem of robust output-feedback control for perturbed uncertain systems. This method uses the basic structure of the conventional EID approach and treats uncertainties and disturbances as a lumped disturbance on the control-input channel. A fixed-time state observer enables state estimation, which resolves the causality issue in an EID-based control system, is finished in a fixed time. An implicit Lyapunov function, the homogeneity with dilation, the input-to-state stability, and the small-gain theorem are used to analyze the convergence and robustness of the EID-based system with measurement noise. Numerical and experimental results are presented to demonstrate the effectiveness and superiority of the proposed method.

Robust Offline Actor-Critic With On-policy Regularized Policy Evaluation

Shuo Cao, Xuesong Wang, Yuhu Cheng

, Available online

[Abstract](25) [FullText HTML] (0) [PDF 2893KB](11)

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To alleviate the extrapolation error and instability inherent in Q-function directly learned by off-policy Q-learning (QL-style) on static datasets, this article utilizes the on-policy state-action-reward-state-action (SARSA-style) to develop an offline reinforcement learning (RL) method named robust offline Actor-Critic with on-policy regularized (OPRAC) policy evaluation. With the help of SARSA-style bootstrap actions, a conservative on-policy Q-function and a penalty term for matching the on-policy and off-policy actions are jointly constructed to regularize the optimal Q-function of off-policy QL-style. This naturally equips the off-policy QL-style policy evaluation with the intrinsic pessimistic conservatism of on-policy SARSA-style, thus facilitating the acquisition of stable estimated Q-function. Even with limited data sampling errors, the convergence of Q-function learned by OPRAC and the controllability of bias upper bound between the learned Q-function and its true Q-value can be theoretically guaranteed. In addition, the sub-optimality of learned optimal policy merely stems from sampling errors. Experiments on the well-known D4RL Gym-MuJoCo benchmark demonstrate that OPRAC can rapidly learn robust and effective task-solving policies owing to the stable estimate of Q-value, outperforming state-of-the-art offline RLs by at least 15%.

A Linear Programming-Based Reinforcement Learning Mechanism for Incomplete-Information Games

Baosen Yang, Changbing Tang, Yang Liu, Guanghui Wen, Guanrong Chen

, Available online , doi: 10.1109/JAS.2024.124464

[Abstract](10) [FullText HTML] (0) [PDF 505KB](2)

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General Lyapunov Stability and Its Application to Time-Varying Convex Optimization

Zhibao Song, Ping Li

, Available online , doi: 10.1109/JAS.2024.124374

[Abstract](155) [FullText HTML] (0) [PDF 1747KB](60)

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In this article, a general Lyapunov stability theory of nonlinear systems is put forward and it contains asymptotic/finite-time/fast finite-time/fixed-time stability. Especially, a more accurate estimate of the settling-time function is exhibited for fixed-time stability, and it is still extraneous to the initial conditions. This can be applied to obtain less conservative convergence time of the practical systems without the information of the initial conditions. As an application, the given fixed-time stability theorem is used to resolve time-varying (TV) convex optimization problem. By the Newton’s method, two classes of new dynamical systems are constructed to guarantee that the solution of the dynamic system can track to the optimal trajectory of the unconstrained and equality constrained TV convex optimization problems in fixed time, respectively. Without the exact knowledge of the time derivative of the cost function gradient, a fixed-time dynamical non-smooth system is established to overcome the issue of robust TV convex optimization. Two examples are provided to illustrate the effectiveness of the proposed TV convex optimization algorithms. Subsequently, the fixed-time stability theory is extended to the theories of predefined-time/practical predefined-time stability whose bound of convergence time can be arbitrarily given in advance, without tuning the system parameters. Under which, TV convex optimization problem is solved. The previous two examples are used to demonstrate the validity of the predefined-time TV convex optimization algorithms.

Two-Stage Approach for Targeted Knowledge Transfer in Self-Knowledge Distillation

Zimo Yin, Jian Pu, Yijie Zhou, Xiangyang Xue

, Available online , doi: 10.1109/JAS.2024.124629

[Abstract](60) [FullText HTML] (0) [PDF 2556KB](18)

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Knowledge distillation (KD) enhances student network generalization by transferring dark knowledge from a complex teacher network. To optimize computational expenditure and memory utilization, self-knowledge distillation (SKD) extracts dark knowledge from the model itself rather than an external teacher network. However, previous SKD methods performed distillation indiscriminately on full datasets, overlooking the analysis of representative samples. In this work, we present a novel two-stage approach to providing targeted knowledge on specific samples, named two-stage approach self-knowledge distillation (TOAST). We first soften the hard targets using class medoids generated based on logit vectors per class. Then, we iteratively distill the under-trained data with past predictions of half the batch size. The two-stage knowledge is linearly combined, efficiently enhancing model performance. Extensive experiments conducted on five backbone architectures show our method is model-agnostic and achieves the best generalization performance. Besides, TOAST is strongly compatible with existing augmentation-based regularization methods. Our method also obtains a speedup of up to 2.95x compared with a recent state-of-the-art method.

Strong Current-State Opacity Verification of Discrete-Event Systems Modeled With Time Labeled Petri Nets

Tao Qin, Li Yin, Gaiyun Liu, Naiqi Wu, Zhiwu Li

, Available online , doi: 10.1109/JAS.2024.124560

[Abstract](28) [FullText HTML] (0) [PDF 1575KB](11)

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This paper addresses the verification of strong current-state opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path (state-event sequence with time information) π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is proposed. Finally, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.

Chattering-Free Fault-Tolerant Cluster Control and Fault Direction Identification for HIL UAV Swarm With Pre-Specified Performance

Pei-Ming Liu, Xiang-Gui Guo, Jian-Liang Wang, Daniel Coutinho, Lihua Xie

, Available online , doi: 10.1109/JAS.2024.124827

[Abstract](54) [FullText HTML] (0) [PDF 2944KB](14)

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In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop (HIL) swarm unmanned aerial vehicles (UAVs) in the presence of possible nonidentical and unknown direction faults (NUDFs) in the yaw channel. The control strategy begins with the design of a pre-specified performance event-triggered observer for each individual UAV. These observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer output. It is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs’ output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.

Unifying Fixed Time and Prescribed Time Control for Strict-Feedback Nonlinear Systems

Xiang Chen, Yujuan Wang, Yongduan Song

, Available online , doi: 10.1109/JAS.2024.124401

[Abstract](22) [FullText HTML] (0) [PDF 1308KB](11)

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This paper investigates the prescribed-time tracking control problem for a class of multi-input multi-output (MIMO) nonlinear strict-feedback systems subject to non-vanishing uncertainties. The inherent unmatched and non-vanishing uncertainties make the prescribed-time control problem become much more nontrivial. The solution to address the challenges mentioned above involves incorporating a prescribed-time filter, as opposed to a finite-time filter, and formulating a prescribed-time Lyapunov stability lemma (Lemma 5). The prescribed-time Lyapunov stability lemma is based on time axis shifting time-varying yet bounded gain, which establishes a novel link between the fixed-time and prescribed-time control method. This allows the restriction condition that the time-varying gain function must satisfy as imposed in most exist prescribed-time control works to be removed. Under the proposed control method, the desire trajectory is ensured to closely track the output of the system in prescribed time. The effectiveness of the theoretical results are verified through numerical simulation.

Linear Programming-Based Consensus of Positive Continuous-Time Multi-Agent Systems

Junfeng Zhang, Fengyu Lin, Shihong Ding, Wei Xing

, Available online

[Abstract](12) [FullText HTML] (0) [PDF 468KB](1)

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Level Curve Tracking via Robust RL-Guided Model Predictive Control

Zhuo Li, Yunlong Guo, Gang Wang, Wei Chen

, Available online

[Abstract](14) [FullText HTML] (0) [PDF 823KB](1)

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Consensus-Based Distributed Secondary Control of Microgrids: A Pre-assigned Time Sliding Mode Approach

Xiangyong Chen, Shunwei Hu, Xiangpeng Xie, Jianlong Qiu

, Available online

[Abstract](9) [FullText HTML] (0) [PDF 546KB](7)

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Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

Min Yang, Guanjun Liu, Ziyuan Zhou, Jiacun Wang

, Available online , doi: 10.1109/JAS.2024.124818

[Abstract](44) [FullText HTML] (0) [PDF 1454KB](14)

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Deep reinforcement learning (DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management. However, due to the model’s inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata, which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications. First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units (PDMUs), and a reverse breadth-first search (BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.

Human Observation-Inspired Universal Image Acquisition Paradigm Integrating Multi-Objective Motion Planning and Control for Robotics

Haotian Liu, Yuchuang Tong, Zhengtao Zhang

, Available online

[Abstract](14) [FullText HTML] (0) [PDF 24624KB](3)

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Image acquisition stands as a prerequisite for scrutinizing surfaces inspection in industrial high-end manufacturing. Current imaging systems often exhibit inflexibility, being confined to specific objects and encountering difficulties with diverse industrial structures lacking standardized computer-aided design (CAD) models or in instances of deformation. Inspired by the multidimensional observation of humans, our study introduces a universal image acquisition paradigm tailored for robotics, seamlessly integrating multi-objective optimization trajectory planning and control scheme to harness measured point clouds for versatile, efficient, and highly accurate image acquisition across diverse structures and scenarios. Specifically, we introduce an energy-based adaptive trajectory optimization (EBATO) method that combines deformation and deviation with dual-threshold optimization and adaptive weight adjustment to improve the smoothness and accuracy of imaging trajectory and posture. Additionally, a multi-optimization control scheme based on a meta-heuristic beetle antennal olfactory recurrent neural network (BAORNN) is proposed to track the imaging trajectory while addressing posture, obstacle avoidance, and physical constraints in industrial scenarios. Simulations, real-world experiments, and comparisons demonstrate the effectiveness and practicality of the proposed paradigm.

A Double Sensitive Fault Detection Filter for Positive Markovian Jump Systems With A Hybrid Event-Triggered Mechanism

Junfeng Zhang, Baozhu Du, Suhuan Zhang, Shihong Ding

, Available online , doi: 10.1109/JAS.2024.124677

[Abstract](31) [FullText HTML] (0) [PDF 2687KB](10)

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This paper is concerned with the double sensitive fault detection filter for positive Markovian jump systems. A new hybrid adaptive event-triggered mechanism is proposed by introducing a non-monotonic adaptive law. A linear adaptive event-triggered threshold is established by virtue of 1-norm inequality. Under such a triggering strategy, the original system can be transformed into an interval uncertain system. By using a stochastic copositive Lyapunov function, an asynchronous fault detection filter is designed for positive Markovian jump systems (PMJSs) in terms of linear programming. The presented filter satisfies both $ L_{-} $-gain ($ \ell_{-} $-gain) fault sensitivity and $ L_{1} $ ($ \ell_{1} $) internal differential privacy sensitivity. The proposed approach is also extended to the discrete-time case. Finally, two examples are provided to illustrate the effectiveness of the proposed design.

Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

Dongfang Li, Jie Huang, Rob Law, Xin Xu, Limin Zhu, Edmond Q. Wu

, Available online , doi: 10.1109/JAS.2024.124632

[Abstract](19) [FullText HTML] (0) [PDF 4070KB](5)

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This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system’s control input and improve the robots’ tracking accuracy. Moreover, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots’ run jitter. Then, Lyapunov’s theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

Revisiting the LQR Problem of Singular Systems

Komeil Nosrati, Juri Belikov, Aleksei Tepljakov, Eduard Petlenkov

, Available online , doi: 10.1109/JAS.2024.124665

[Abstract](51) [FullText HTML] (0) [PDF 3113KB](11)

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In the development of linear quadratic regulator (LQR) algorithms, the Riccati equation approach offers two important characteristics—it is recursive and readily meets the existence condition. However, these attributes are applicable only to transformed singular systems, and the efficiency of the regulator may be undermined if constraints are violated in nonsingular versions. To address this gap, we introduce a direct approach to the LQR problem for linear singular systems, avoiding the need for any transformations and eliminating the need for regularity assumptions. To achieve this goal, we begin by formulating a quadratic cost function to derive the LQR algorithm through a penalized and weighted regression framework and then connect it to a constrained minimization problem using the Bellman’s criterion. Then, we employ a dynamic programming strategy in a backward approach within a finite horizon to develop an LQR algorithm for the original system. To accomplish this, we address the stability and convergence analysis under the reachability and observability assumptions of a hypothetical system constructed by the pencil of augmented matrices and connected using the Hamiltonian diagonalization technique.

High-Order Fully Actuated System Models for Strict-Feedback Systems With Increasing Dimensions

Xiang Xu, Guang-Ren Duan

, Available online , doi: 10.1109/JAS.2024.124599

[Abstract](23) [FullText HTML] (0) [PDF 878KB](9)

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This paper mainly addresses control problems of strict-feedback systems (SFSs) with increasing dimensions. Compared with the commonly-considered SFSs where the subsystems have the same dimension, we aim to handle more complex cases, i.e., the subsystems in the considered SFSs are assumed to have increasing dimensions. By transforming the systems into high-order fully-actuated system (HOFAS) models, the stabilizing controllers can be directly given. Besides first-order SFSs, second-order and high-order SFSs are also considered.

Safe Q-Learning for Data-Driven Nonlinear Optimal Control With Asymmetric State Constraints

Mingming Zhao, Ding Wang, Shijie Song, Junfei Qiao

, Available online , doi: 10.1109/JAS.2024.124509

[Abstract](69) [FullText HTML] (0) [PDF 3003KB](28)

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This article develops a novel data-driven safe Q-learning method to design the safe optimal controller which can guarantee constrained states of nonlinear systems always stay in the safe region while providing an optimal performance. First, we design an augmented utility function consisting of an adjustable positive definite control obstacle function and a quadratic form of the next state to ensure the safety and optimality. Second, by exploiting a pre-designed admissible policy for initialization, an off-policy stabilizing value iteration Q-learning (SVIQL) algorithm is presented to seek the safe optimal policy by using offline data within the safe region rather than the mathematical model. Third, the monotonicity, safety, and optimality of the SVIQL algorithm are theoretically proven. To obtain the initial admissible policy for SVIQL, an offline VIQL algorithm with zero initialization is constructed and a new admissibility criterion is established for immature iterative policies. Moreover, the critic and action networks with precise approximation ability are established to promote the operation of VIQL and SVIQL algorithms. Finally, three simulation experiments are conducted to demonstrate the virtue and superiority of the developed safe Q-learning method.

Release Power of Mechanism and Data Fusion: A Hierarchical Strategy for Enhanced MIQ-Related Modeling and Fault Detection in BFIP

Siwei Lou, Chunjie Yang, Zhe Liu, Shaoqi Wang, Hanwen Zhang, Ping Wu

, Available online , doi: 10.1109/JAS.2024.124821

[Abstract](38) [FullText HTML] (0) [PDF 18758KB](16)

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Data-driven techniques are reshaping blast furnace iron-making process (BFIP) modeling, but their “black-box” nature often obscures interpretability and accuracy. To overcome these limitations, our mechanism and data co-driven strategy (MDCDS) enhances model transparency and molten iron quality (MIQ) prediction. By zoning the furnace and applying mechanism-based features for material and thermal trends, coupled with a novel stationary broad feature learning system (StaBFLS), interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined. Subsequently, by integrating stationary feature representation with mechanism features, our temporal matching broad learning system (TMBLS) aligns process and quality variables using MIQ as the target. This integration allows us to establish process monitoring statistics using both mechanism and data-driven features, as well as detect modeling deviations. Validated against real-world BFIP data, our MDCDS model demonstrates consistent process alignment, robust feature extraction, and improved MIQ modeling—yielding better fault detection. Additionally, we offer detailed insights into the validation process, including parameter baselining and optimization. Details of the code are available online.¹

Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

Kui Jiang, Ruoxi Wang, Yi Xiao, Junjun Jiang, Xin Xu, Tao Lu

, Available online

[Abstract](92) [FullText HTML] (0) [PDF 1977KB](28)

Abstract:
Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network (PerTeRNet). It contains two sub-networks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery, we develop a novel perturbation-guided texture enhancement module (PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://github.com/kuijiang94/PerTeRNet.

An Improved Repetitive-Control System Using A Complex-Coefficient Filter

Qicheng Mei, Jinhua She, Fei Long, Yanjun Shen

, Available online , doi: 10.1109/JAS.2024.124554

[Abstract](44) [FullText HTML] (0) [PDF 873KB](16)

Abstract:

Multi-USV Formation Collision Avoidance via Deep Reinforcement Learning and COLREGs

Cheng-Cheng Wang, Yu-Long Wang, Li Jia

, Available online

[Abstract](19) [FullText HTML] (0) [PDF 887KB](13)

Abstract:

Distributed Observer for Full-Measured Nonlinear Systems Based on Knowledge of FMCF

Haotian Xu, Shuai Liu, Yueyang Li, Ke Li

, Available online , doi: 10.1109/JAS.2024.124467

[Abstract](35) [FullText HTML] (0) [PDF 2820KB](20)

Abstract:
Driven by practical applications, the achievement of distributed observers for nonlinear systems has emerged as a crucial advancement in recent years. However, existing theoretical advancements face certain limitations: they either fail to address more complex nonlinear phenomena, rely on hard-to-verify assumptions, or encounter difficulties in solving system parameters. Consequently, this paper aims to address these challenges by investigating distributed observers for nonlinear systems through the full-measured canonical form (FMCF), which is inspired by full-measured system (FMS) theory. To begin with, this study addresses the fact that the FMCF can only be obtained through the observable canonical form (OCF) in existing FMS theories. The paper demonstrates that a class of nonlinear systems can directly obtain FMCF through state space equations, independent of OCF. Also, a general method for solving FMCF in such systems is provided. Furthermore, based on the FMCF, A distributed observer is developed for nonlinear systems under two scenarios: Lipschitz conditions and open-loop bounded conditions. The paper establishes their asymptotic omniscience and demonstrates that the designed distributed observer in this study has fewer design parameters and is more convenient to construct than existing approaches. Finally, the effectiveness of the proposed methods is validated through simulation results on Van der Pol oscillators and microgrid systems.

Federated Experiments: Generative Causal Inference Powered by LLM-based Agents Simulation and RAG-based Domain Docking

De-Yu Zhou, Xiao Xue, Qun Ma, Chao Guo, Li-Zhen Cui, Yong-Lin Tian, Jing Yang, Fei-Yue Wang

, Available online , doi: 10.1109/JAS.2024.124671

[Abstract](47) [FullText HTML] (0) [PDF 682KB](39)

Abstract:

Distributed State and Fault Estimation for Cyber-Physical Systems Under DoS Attacks

Limei Liang, Rong Su, Haotian Xu

, Available online , doi: 10.1109/JAS.2024.124527

[Abstract](46) [FullText HTML] (0) [PDF 507KB](25)

Abstract:

Prediction-Based State Estimation and Compensation Control for Networked Systems With Communication Constraints and DoS Attacks

Zhong-Hua Pang, Qian Cao, Haibin Guo, Zhe Dong

, Available online

[Abstract](16) [FullText HTML] (0) [PDF 427KB](22)

Abstract:

Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning

Yuanxin Lin, Zhiwen Yu, Kaixiang Yang, Ziwei Fan, C. L. Philip Chen

, Available online , doi: 10.1109/JAS.2024.124557

[Abstract](27) [FullText HTML] (0) [PDF 1603KB](4)

Abstract:
Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system (MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system (MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.

On Zero Dynamics and Controllable Cyber-Attacks in Cyber-Physical Systems and Dynamic Coding Schemes as Their Countermeasures

Mahdi Taheri, Khashayar Khorasani, Nader Meskin

, Available online , doi: 10.1109/JAS.2024.124692

[Abstract](67) [FullText HTML] (0) [PDF 1735KB](24)

Abstract:
In this paper, we study stealthy cyber-attacks on actuators of cyber-physical systems (CPS), namely zero dynamics and controllable attacks. In particular, under certain assumptions, we investigate and propose conditions under which one can execute zero dynamics and controllable attacks in the CPS. The above conditions are derived based on the Markov parameters of the CPS and elements of the system observability matrix. Consequently, in addition to outlining the number of required actuators to be attacked, these conditions provide one with the minimum system knowledge needed to perform zero dynamics and controllable cyber-attacks. As a countermeasure against the above stealthy cyber-attacks, we develop a dynamic coding scheme that increases the minimum number of the CPS required actuators to carry out zero dynamics and controllable cyber-attacks to its maximum possible value. It is shown that if at least one secure input channel exists, the proposed dynamic coding scheme can prevent adversaries from executing the zero dynamics and controllable attacks even if they have complete knowledge of the coding system. Finally, two illustrative numerical case studies are provided to demonstrate the effectiveness and capabilities of our derived conditions and proposed methodologies.

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](72) [FullText HTML] (0) [PDF 825KB](26)

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Data-Driven Iterative Learning Consensus Tracking Based on Robust Neural Models for Unknown Heterogeneous Nonlinear Multiagent Systems With Input Constraints

Chong Zhang, Yunfeng Hu, TingTing Wang, Xun Gong, Hong Chen

, Available online

[Abstract](41) [FullText HTML] (0) [PDF 409KB](15)

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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](53) [FullText HTML] (0) [PDF 878KB](19)

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](86) [FullText HTML] (0) [PDF 4081KB](36)

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

Jiufang Chen, Kechen Liu, Xin Luo, Ye Yuan, Khaled Sedraoui, Yusuf Al-Turki, MengChu Zhou

, Available online

[Abstract](26) [FullText HTML] (0) [PDF 1437KB](9)

Abstract:
High-dimensional and incomplete (HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis (LFA) model is capable of conducting efficient representation learning to an HDI matrix, whose hyper-parameter adaptation can be implemented through a particle swarm optimizer (PSO) to meet scalable requirements. However, conventional PSO is limited by its premature issues, which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle’s state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer (SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency. Hence, SPSO’s use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

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](173) [FullText HTML] (0) [PDF 997KB](33)

Abstract:

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](170) [FullText HTML] (0) [PDF 1585KB](13)

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.

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](77) [FullText HTML] (0) [PDF 2534KB](35)

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](80) [FullText HTML] (0) [PDF 564KB](37)

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](45) [FullText HTML] (0) [PDF 428KB](7)

Abstract:

Semi-Decentralized Convex Optimization on

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Weijian Li, Peng Yi

, Available online , doi: 10.1109/JAS.2024.124356

[Abstract](46) [FullText HTML] (0) [PDF 377KB](11)

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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](32) [FullText HTML] (0) [PDF 356KB](9)

Abstract:

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](79) [FullText HTML] (0) [PDF 1560KB](23)

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](65) [FullText HTML] (0) [PDF 3816KB](10)

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.