International Journal of Adaptive Control and Signal Processing最新文献

{"title":"AI and Machine Learning for Control Applications","authors":"Jiusun Zeng, Shaohan Chen, Xiaoyu Zhang, Chuanhou Gao","doi":"10.1002/acs.4026","DOIUrl":"https://doi.org/10.1002/acs.4026","url":null,"abstract":"<p>The rapid advancement of artificial intelligence (AI) and machine learning technologies has fundamentally changed the traditional paradigm of control engineering. The focus of this special issue was to inspire people to discuss how AI and machine learning techniques can be used to enhance control applications in a wide range of fields, such as industrial process monitoring and fault diagnosis, optimal process design and control, deep generative model-based target recognition, and so forth. The varieties of methodologies and application studies within this special issue fully revealed the potential and necessity to further promote control-oriented AI and machine learning techniques. It is believed that this subject will continue to flourish and become one of the centerpieces of control research communities.</p><p>Among the papers accepted in the special issue, the first element to emerge is the development of AI and machine learning techniques for industrial process monitoring and anomaly localization [<span>1-3</span>]. Modern industrial processes often exhibit complicated characteristics of time-varying, multi-unit collaboration, multi-rate measurements, and significant process noises. There is an urgent need to understand and handle these characteristics. People within the study by Wu et al. [<span>3</span>] developed an adaptive spatiotemporal decouple graph convolution network to deal with the time-varying characteristics of large-scale process. The adaptive spatiotemporal graph is capable of incorporating prior knowledge and better reflecting the dynamic relationships among process variables. The proposed feature redundancy reduction scheme can simplify the graph structure and results in a more interpretable model. The enhanced fault detection performance revealed the potential of the adaptive graph neural network in industrial process monitoring. A further research issue is the multi-unit collaboration and multi-rate measurements in industrial processes. The work of Dong et al. [<span>1</span>] introduced a subsystem decomposition method and the multi-rate partial least squares, which showed promising performance in identifying process faults. In handling process noises, Jia et al. [<span>2</span>] introduced a slow feature-constrained decomposition autoencoder for anomaly detection isolation in industrial processes, which reduced the high-frequency noise and translated into better fault detection performance and isolation accuracy.</p><p>The second element discussed by the papers within this special issue is fault diagnosis and performance degradation prediction of rotating machinery and fuel cell stack [<span>4-10</span>]. Despite the numerous research progress made in fault diagnosis of rotating machinery in recent years, there is still a lack of effective solution to address issues like domain drift and unknown faults, data imbalance, strong noise, and so forth. Lin et al. [<span>4</span>] introduced a few-shot learning-based unknown","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 7","pages":"1362-1363"},"PeriodicalIF":3.9,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acs.4026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predefined-Time Prescribed Performance Fault-Tolerant Control for a Class of Uncertain Nonlinear Systems Under Full State Constraints","authors":"Nuan Shao,&nbsp;Shanghua Wu,&nbsp;Yinghao Xie,&nbsp;Le Liu,&nbsp;Yiming Fang","doi":"10.1002/acs.4010","DOIUrl":"https://doi.org/10.1002/acs.4010","url":null,"abstract":"<p>This study introduces a fault-tolerant control approach aimed at enhancing the control performance of a certain type of nonlinear systems that are prone to actuator faults and external disturbances. This combined fault disturbance term, along with the systems' external disturbance terms, are estimated by specially designed predefined-time observers. To achieve fault tolerance, a predefined-time fault-tolerant controller is designed by integrating the asymmetric barrier Lyapunov function (ABLF) with the improved prescribed performance function (IPPF). The ABLF is utilized to address the issue of full state constraints, while the IPPF ensures that the system states meet specific performance requirements. Additionally, novel command filters are introduced to alleviate the “explosion of complexity” issue, thereby reducing the system's computational burden. The theoretical analysis demonstrates that the proposed method drives the closed-loop system to converge to a neighborhood near the equilibrium point within a predefined time, while also guaranteeing that all system states remain within specified constraint boundaries. Finally, the validity and feasibility of the proposed method are validated through simulations and dSPACE experiments.</p>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 6","pages":"1335-1350"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensor Fault Detection and Isolation in Multi-Agent Systems","authors":"Kaustav Jyoti Borah","doi":"10.1002/acs.3984","DOIUrl":"https://doi.org/10.1002/acs.3984","url":null,"abstract":"<div>\u0000 \u0000 <p>Sensor fault detection and isolation in multi-agent systems (MAS) with uncertain dynamics and undirected, connected communication networks is addressed in this article. The proposed approach involves a two-step process: First, fault detection, and then likelihood-based fault isolation. A novel fault reconstruction technique is introduced by tuning the unscented Kalman filter (UKF) noise covariance matrices within the <i>Q</i>-learning framework. This adjustment helps reconstruct the uncertain states of the MAS and train the internal parameters of a neural network using historical measurements. This innovative method is referred to as Enhanced reinforced UKF (ERUKF). To reduce neural network approximation errors, a robust control term utilizing the hyperbolic tangent function is applied. The stability of ERUKF, when combined with the robust control method, is mathematically proven using the Lyapunov theorem. Simulations illustrate that ERUKF exhibits lower estimation errors compared to adaptive UKF, achieving a 96.67% success rate in fault isolation under Monte Carlo (MC) simulations.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 5","pages":"952-964"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fixed-Time Adaptive Tracking Control for High-Order Nonlinear Systems With Asymmetric Output Constraints","authors":"Zhaoyang Han,&nbsp;Zong-Yao Sun,&nbsp;Jiao-Jiao Li,&nbsp;Chih-Chiang Chen,&nbsp;Qinghua Meng","doi":"10.1002/acs.4011","DOIUrl":"https://doi.org/10.1002/acs.4011","url":null,"abstract":"<div>\u0000 \u0000 <p>This article proposes a fixed-time adaptive tracking control strategy for a class of high-order non-strict feedback nonlinear systems with asymmetric output constraints. The key technique of this strategy is to guarantee that the output of the system does not break the pre-set constraints through the barrier Lyapunov function (BLF) while approximating any unknown nonlinear term through the fuzzy logic systems (FLSs). We design a fixed-time fuzzy adaptive controller to ensure that the closed-loop error signal converges to a tunable neighborhood near zero in a fixed-time instant. Compared with the existing results, the fixed-time adaptive control is realized and the semi-global fixed-time stability of the closed-loop system is proved theoretically. Finally, the effectiveness of the proposed scheme is verified by a numerical simulation.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 6","pages":"1351-1360"},"PeriodicalIF":3.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Command-Filter-Based Adaptive Tracking Control of Uncertain Stochastic Nonlinear Systems With Multiple Constraints","authors":"Heqin Tang,&nbsp;Lidong Wang,&nbsp;Zhongfeng Li,&nbsp;Aosen Zhang,&nbsp;Long Gu","doi":"10.1002/acs.4008","DOIUrl":"https://doi.org/10.1002/acs.4008","url":null,"abstract":"<div>\u0000 \u0000 <p>An adaptive preset performance tracking control scheme is proposed for a class of uncertain strict feedback stochastic nonlinear systems with full state constraints and input dead zones. A new coordinate transformation and obstacle Lyapunov function are adopted to solve the problem of full state constraints and preset performance. The nonlinearity of the input dead zone is solved by splitting the dead zone nonlinear term into a linear term and a bounded disturbance term. A fuzzy logic system is used to approximate the unknown function in the system. A command filter is introduced to solve the “differential explosion” problem, and a compensation signal is used to compensate for the filtering error. An adaptive command filter controller is designed using the adaptive backstepping method and stochastic stability theory, which can ensure that all the closed-loop signals are ultimately bounded with semiglobal consistency in terms of the probability of quarter moments of an appropriate compact set. Meanwhile, the tracking error satisfies the predefined performance and converges to a small neighborhood close to zero. Simulation results show the effectiveness of the method.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 6","pages":"1308-1318"},"PeriodicalIF":3.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predefined-Time Tracking Control for Heterogeneous Vehicle Platoons Under Uncertain Initial Conditions Based on a Disturbance Observer","authors":"Zhankui Song,&nbsp;Yong Yu","doi":"10.1002/acs.4009","DOIUrl":"https://doi.org/10.1002/acs.4009","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper is concerned with the predefined-time tracking control problem for vehicular platoon systems composed of heterogeneous vehicles with arbitrary initial states under the influence of nonlinear disturbances. To counteract the influence of system uncertainties and disturbances, a practical predefined-time disturbance observer (PTDO) is constructed for lumped disturbance compensation in the closed-loop system. Meanwhile, a predefined-time control scheme is developed incorporating a predefined-time performance function (PTPF) and a composite error transformation mechanism to ensure that the position and velocity errors of the vehicle platoon system are constrained within a predetermined accuracy within a predefined time frame. The proposed approach eliminates reliance on initial conditions inherent to conventional prescribed performance control methods while demonstrating adaptability to diverse application scenarios. The efficacy and practicality of the developed control protocol are substantiated through rigorous theoretical analysis and numerical simulations.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 6","pages":"1319-1334"},"PeriodicalIF":3.9,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low Complexity Distributed Adaptive Bipartite Tracking Consensus Control of Nonlinear Multi-Agent Systems Against Sensor Deception Attacks","authors":"Xinyu Xu,&nbsp;Xinjun Wang,&nbsp;Ben Niu,&nbsp;Shengtao Li,&nbsp;Xiaohua Liu","doi":"10.1002/acs.4002","DOIUrl":"https://doi.org/10.1002/acs.4002","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, a novel perspective on an adaptive bipartite consensus tracking scheme for multi-agent systems (MASs) in the face of sensor attacks is presented. The bipartite consensus tracking problem of MASs is transformed into the tracking problem of a single agent based on the shortest path method, and the assumption of structural balance for a digraph is removed. A significant challenge encountered in this article is that the original sensor measurement states cannot be directly and accurately measured and used after being subjected to malicious deception attacks. Therefore, to solve this issue, a novel coordinate transformation method including compromised states is proposed. Furthermore, the Nussbaum function is only introduced in the final step to avoid the effects of unknown attack gains resulting from deception attacks in the system, and a set of tuning functions are designed to resolve the singularity issue encountered during the adaptive iterative process of previous studies. A distributed security control scheme is designed using backstepping and Lyapunov stability theory, ensuring that all closed-loop system signals remain globally and uniformly bounded. Simulation results with a group of single-link robots validate the efficacy of the presented approach.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 6","pages":"1208-1220"},"PeriodicalIF":3.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fixed-Time Convergence Adaptive Robust Controller for Overhead Cranes Based on Reinforcement Learning Considering Input Saturation","authors":"Junren Wei,&nbsp;Weimin Xu","doi":"10.1002/acs.4005","DOIUrl":"https://doi.org/10.1002/acs.4005","url":null,"abstract":"<div>\u0000 \u0000 <p>An adaptive fixed-time extended state observer (AFESO)-based nonsingular fast terminal sliding mode control (NFTSMC) method is proposed for a class of underactuated overhead crane systems with unknown external disturbances. Firstly, an improved fixed-time nonsingular fast terminal sliding mode surface is designed, which not only solves the singularity problems in terminal sliding mode control but also achieves system stabilization within bounded convergence time. Moreover, an adaptive fixed-time extended state observer is presented to estimate both matched and unmatched disturbances; then, the estimated value is used for feedforward compensation to compose the controller. Subsequently, to improve control quality and enhance robustness of the control system, a weakly reinforcement learning (RL) algorithm is used to update parameters of a chattering free reaching law parameter, upon which a novel fixed-time auxiliary error compensation system is constructed to solve the input saturation problem. The fixed-time stability analysis of the closed-loop system is derived by Lyapunov theory. Finally, the rapidity and effectiveness of the developed control method are demonstrated through simulation results.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 6","pages":"1252-1273"},"PeriodicalIF":3.9,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite-Time Fuzzy Adaptive Control for Nonlinear Systems With Asymmetric Dead-Zone and Actuator Faults and via an Event-Triggered Mechanism","authors":"Mohamed Kharrat","doi":"10.1002/acs.4007","DOIUrl":"https://doi.org/10.1002/acs.4007","url":null,"abstract":"<div>\u0000 \u0000 <p>This study focuses on adaptive fuzzy finite-time control for nonstrict-feedback nonlinear systems afflicted with actuator faults and asymmetric dead-zone, utilizing an event-triggered approach. Fuzzy logic systems are used to approximate the system's unknown terms. A relative threshold event-triggering mechanism is devised to reduce communication requirements. This mechanism ensures that the actuator only receives system input when a significant event occurs, thereby optimizing control efficiency. The proposed strategy integrates event-triggered adaptive fuzzy control with the backstepping method to achieve finite-time stability. This approach guarantees that the tracking error converges to a region near the origin, and ensures semiglobal practical finite-time stability for all signals within the closed-loop system. A numerical example and a real-world example of a pendulum system are used to illustrate the efficacy of the control approach.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 6","pages":"1294-1307"},"PeriodicalIF":3.9,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Gain Scheduled Reentry Control for Reusable Launch Vehicles Based on Active Estimation and Classified Compensation","authors":"Yongshuai Wang,&nbsp;Mingwei Sun,&nbsp;Chengyi Xia,&nbsp;Zengqiang Chen","doi":"10.1002/acs.4006","DOIUrl":"https://doi.org/10.1002/acs.4006","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes an adaptive gain scheduled attitude control strategy for the reentry phase of reusable launch vehicles (RLVs), which is model-independent and offers a convenient implementation. It originally integrates an extended state observer (ESO) with a sigmoid estimator to address the wide envelope of velocities and altitudes, along with the strong nonlinearities and aerodynamic uncertainties. In particular, the ESO is used for disturbance estimation, whereas the sigmoid estimator is applied for disturbance classification. The proposed design achieves the adaptive online estimation and compensation of the RLV control gain and reduces the model dependence. In addition, the closed-loop finite-gain stability is investigated based on the Lyapunov theory, and the tracking boundedness can be proved. Finally, the effectiveness and robustness of the proposed design are verified based on numerical simulations and Monte Carlo tests, and the advantages of weak model dependence and strong ability on disturbance rejection are highlighted in the proposed design compared with the PID control and linear active disturbance rejection control.</p>\u0000 </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"39 6","pages":"1274-1293"},"PeriodicalIF":3.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}