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Switching dynamic event-triggered disturbance rejection control for uncertain Lipschitz nonlinear system using recursive observers

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.02.012

Guanghao Su , Zhenlei Wang

This paper investigates the event-triggered disturbance rejection control problem for a class of uncertain Lipschitz nonlinear systems with time-varying disturbances. The design process to address this problem involves the construction of a state and disturbance joint observer, along with the design of an event-triggered controller that incorporates a designable minimum inter-event time. To design the joint observer, time-varying disturbances are initially transformed into multiple intermediate variables by employing a linear combination of system states and disturbance derivatives. Subsequently, the recursive observers are constructed to obtain intermediate variable estimations, while the recursive mechanism is utilized to compensate for the extra derivative term arising from disturbance transformation. Then, states and disturbance estimations are used to develop the event-triggered disturbance rejection controller. The event-triggered protocol includes a mandatory resting interval and a dynamic trigger variable that monotonically increases during the resting interval, ensuring a guaranteed minimum inter-event time while also decreasing the trigger frequency. Finally, we provide numerical examples to showcase the effectiveness of the proposed method. Simulation results show that the proposed method is able to reduce the steady-state error by 59.6% and extend the minimum trigger interval by at least 2.44 times compared to existing methods.

{"title":"Switching dynamic event-triggered disturbance rejection control for uncertain Lipschitz nonlinear system using recursive observers","authors":"Guanghao Su , Zhenlei Wang","doi":"10.1016/j.isatra.2025.02.012","DOIUrl":"10.1016/j.isatra.2025.02.012","url":null,"abstract":"<div><div>This paper investigates the event-triggered disturbance rejection control problem for a class of uncertain Lipschitz nonlinear systems with time-varying disturbances. The design process to address this problem involves the construction of a state and disturbance joint observer, along with the design of an event-triggered controller that incorporates a designable minimum inter-event time. To design the joint observer, time-varying disturbances are initially transformed into multiple intermediate variables by employing a linear combination of system states and disturbance derivatives. Subsequently, the recursive observers are constructed to obtain intermediate variable estimations, while the recursive mechanism is utilized to compensate for the extra derivative term arising from disturbance transformation. Then, states and disturbance estimations are used to develop the event-triggered disturbance rejection controller. The event-triggered protocol includes a mandatory resting interval and a dynamic trigger variable that monotonically increases during the resting interval, ensuring a guaranteed minimum inter-event time while also decreasing the trigger frequency. Finally, we provide numerical examples to showcase the effectiveness of the proposed method. Simulation results show that the proposed method is able to reduce the steady-state error by 59.6% and extend the minimum trigger interval by at least 2.44 times compared to existing methods.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 103-112"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Equivalent-input-disturbance based rotating control of drill-string in compound directional drilling process subject to coal seam variation

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.02.011

Wangnian Li , Shipeng Chen , Chengda Lu , Lingfeng Mu , Aoxue Yang , Youzhen Zhang , Haitao Song , Ningping Yao , Hongliang Tian , Min Wu

Coal seam variations in coal mine drilling can cause fluctuations in feed speed and rotational speed of the rotary head (ROR), reducing the performance of compound directional drilling control systems. This paper addresses these issues by considering the change in intrinsic specific energy caused by coal seam variations as a disturbance. An equivalent-input-disturbance (EID) based control system is designed to mitigate these effects. An axial-torsional coupled dynamic model of the drill-string system is developed, and EID and improved EID estimators are used to suppress the impact of bit–rock interaction. Field data from a gas extraction borehole is used to validate the effectiveness of the drill-string dynamics model. Simulation results demonstrate that the proposed control system surpasses the PI control system, the disturbance observer-based control system, and the active disturbance rejection control system. Specifically, the feed speed overshoot is reduced from 73%, 47%, and 46% to 33%, with settling time reduced from 3 s, 1.7 s, and 1.8 s to 1.5 s. The ROR overshoot decreases from 54%, 49%, and 43% to 11%, and settling time from 4.7 s, 3.1 s, and 2.8 s to 2.3 s. Additionally, feed speed and ROR fluctuations are minimized to 0.2% and 0.1%, respectively, demonstrating superior performance.

{"title":"Equivalent-input-disturbance based rotating control of drill-string in compound directional drilling process subject to coal seam variation","authors":"Wangnian Li , Shipeng Chen , Chengda Lu , Lingfeng Mu , Aoxue Yang , Youzhen Zhang , Haitao Song , Ningping Yao , Hongliang Tian , Min Wu","doi":"10.1016/j.isatra.2025.02.011","DOIUrl":"10.1016/j.isatra.2025.02.011","url":null,"abstract":"<div><div>Coal seam variations in coal mine drilling can cause fluctuations in feed speed and rotational speed of the rotary head (ROR), reducing the performance of compound directional drilling control systems. This paper addresses these issues by considering the change in intrinsic specific energy caused by coal seam variations as a disturbance. An equivalent-input-disturbance (EID) based control system is designed to mitigate these effects. An axial-torsional coupled dynamic model of the drill-string system is developed, and EID and improved EID estimators are used to suppress the impact of bit–rock interaction. Field data from a gas extraction borehole is used to validate the effectiveness of the drill-string dynamics model. Simulation results demonstrate that the proposed control system surpasses the PI control system, the disturbance observer-based control system, and the active disturbance rejection control system. Specifically, the feed speed overshoot is reduced from 73%, 47%, and 46% to 33%, with settling time reduced from 3 s, 1.7 s, and 1.8 s to 1.5 s. The ROR overshoot decreases from 54%, 49%, and 43% to 11%, and settling time from 4.7 s, 3.1 s, and 2.8 s to 2.3 s. Additionally, feed speed and ROR fluctuations are minimized to 0.2% and 0.1%, respectively, demonstrating superior performance.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 352-363"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement learning-based trajectory tracking optimal control of unmanned surface vehicles in narrow water areas

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.01.045

Ziping Wei, Jialu Du

For unmanned surface vehicles (USVs) navigating in narrow water areas in the presence of unknown dynamics and ocean environmental disturbances, this paper develops a reinforcement learning (RL)-based optimal control scheme for the trajectory tracking of USVs under motion state constraints. A nonlinear map is introduced to transform constrained motion state errors into bounded transformed errors, and then the motion state-constrained trajectory tracking problem of USVs is equivalently transformed into a boundedness problem of the transformed errors. Furthermore, an actor-critic framework is developed by utilizing adaptive neural networks (NNs). Within the actor-critic framework, a novel weight update law is designed for the critic NN by combining the gradient descent approach and the concurrent learning technology, thereby relaxing the persistent excitation condition required for adaptive critic NN weight updates. Subsequently, a disturbance compensator is designed and combined with the actor-critic framework to learn the trajectory tracking optimal control law for USVs in the presence of unknown dynamics and disturbances. Finally, theoretical analyses prove that the developed control scheme guarantees the boundedness of all signals in the USV closed-loop trajectory tracking control system, and simulation results show that the developed control scheme can make USVs track the desired trajectory in narrow water areas while reducing the energy consumption by approximately 14.6 % compared with an existing controller.

{"title":"Reinforcement learning-based trajectory tracking optimal control of unmanned surface vehicles in narrow water areas","authors":"Ziping Wei, Jialu Du","doi":"10.1016/j.isatra.2025.01.045","DOIUrl":"10.1016/j.isatra.2025.01.045","url":null,"abstract":"<div><div>For unmanned surface vehicles (USVs) navigating in narrow water areas in the presence of unknown dynamics and ocean environmental disturbances, this paper develops a reinforcement learning (RL)-based optimal control scheme for the trajectory tracking of USVs under motion state constraints. A nonlinear map is introduced to transform constrained motion state errors into bounded transformed errors, and then the motion state-constrained trajectory tracking problem of USVs is equivalently transformed into a boundedness problem of the transformed errors. Furthermore, an actor-critic framework is developed by utilizing adaptive neural networks (NNs). Within the actor-critic framework, a novel weight update law is designed for the critic NN by combining the gradient descent approach and the concurrent learning technology, thereby relaxing the persistent excitation condition required for adaptive critic NN weight updates. Subsequently, a disturbance compensator is designed and combined with the actor-critic framework to learn the trajectory tracking optimal control law for USVs in the presence of unknown dynamics and disturbances. Finally, theoretical analyses prove that the developed control scheme guarantees the boundedness of all signals in the USV closed-loop trajectory tracking control system, and simulation results show that the developed control scheme can make USVs track the desired trajectory in narrow water areas while reducing the energy consumption by approximately 14.6 % compared with an existing controller.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 152-164"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel zonotopic Kalman filter-based actuator fault detection for time delay systems

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.01.037

Yu-Qing Ma , Zi-Yun Wang , Yan Wang , Ju H. Park , Zhi-Cheng Ji

This paper addresses the problems of state estimation and actuator fault diagnosis in linear discrete time delay systems with actuator faults, and a zonotopic Kalman filter-based actuator fault detection (Z-KF-AFD) algorithm is proposed. First, the relationship between faults and states is calculated by approximating small-range noise. Next, by wrapping non-zero initial states with delays and zero initial faults within a zonotopic set, the zonotopic Kalman filter (ZKF) is iteratively derived to establish the association between current and delayed data. The optimal observer estimator gain for the optimal ZKF is then designed by minimizing the size of the zonotopic set. Subsequently, the fault zonotopic set, which links the state at the current time to the delayed state, is separated. Fault detection is achieved by determining whether zero lies within the bounds of the estimated fault zonotope. Finally, the feasibility of the proposed algorithm is validated through fault diagnosis examples involving a numerical system and a bidirectional DC–DC converter system.

引用次数: 0

Minimum maximum regularized multiscale convolutional neural network and its application in intelligent fault diagnosis of rotary machines

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.01.044

Yaochun Wu , Shaohua Du , Guijun Wu , Xiaobo Guo , Jie Wu , Rongzheng Zhao , Chi Ma

Convolutional neural networks (CNN) have achieved significant advancements in intelligent fault diagnosis of rotary machines. However, the limitations of using a single scale convolution kernel in convolutional layer and the exclusive focus on classification accuracy by the cross-entropy loss function during model training result in suboptimal diagnostic performance and generalization ability of CNNs in environments with strong background noise and imbalanced data. To address these challenges, a fault recognition method for rotary machines utilizing a minimum maximum regularized multiscale CNN (MMRMCNN) is proposed. A multiscale feature extraction module is devised, which uses convolutional layers with diverse scale kernels to capture multiscale features form input data. Additionally, a minimum maximum regularized objective function is introduced to augment the original cross-entropy loss function. This modification enables the model to consider not only recognition accuracy but also the compactness within classes and separation between classes of learning features during network training. The proposed approach effectively narrows the intra class margin of device health status features while widening the inter class margin, thereby mitigating the impact of noise and data imbalance on the mapping of health status relationship. Performance evaluation of the MMRMCNN is conducted using a measured dataset, the PU bearing dataset, and a rotor dataset. We found that the fault recognition accuracy of the proposed method exceeds 97.79 %, and the accuracy of fault recognition under noisy background and unbalanced data conditions is also above 94.81 % and 94.72 %, respectively. This demonstrate the superior fault recognition capabilities of the proposed method in scenarios characterized by strong background noise and data imbalance. Overall, the results attest to the exceptional performances of the developed MMRMCNN in fault recognition under challenging conditions, underscoring its potential in advancing the field of in Telligent fault diagnosis for rotary machines.

{"title":"Minimum maximum regularized multiscale convolutional neural network and its application in intelligent fault diagnosis of rotary machines","authors":"Yaochun Wu , Shaohua Du , Guijun Wu , Xiaobo Guo , Jie Wu , Rongzheng Zhao , Chi Ma","doi":"10.1016/j.isatra.2025.01.044","DOIUrl":"10.1016/j.isatra.2025.01.044","url":null,"abstract":"<div><div>Convolutional neural networks (CNN) have achieved significant advancements in intelligent fault diagnosis of rotary machines. However, the limitations of using a single scale convolution kernel in convolutional layer and the exclusive focus on classification accuracy by the cross-entropy loss function during model training result in suboptimal diagnostic performance and generalization ability of CNNs in environments with strong background noise and imbalanced data. To address these challenges, a fault recognition method for rotary machines utilizing a minimum maximum regularized multiscale CNN (MMRMCNN) is proposed. A multiscale feature extraction module is devised, which uses convolutional layers with diverse scale kernels to capture multiscale features form input data. Additionally, a minimum maximum regularized objective function is introduced to augment the original cross-entropy loss function. This modification enables the model to consider not only recognition accuracy but also the compactness within classes and separation between classes of learning features during network training. The proposed approach effectively narrows the intra class margin of device health status features while widening the inter class margin, thereby mitigating the impact of noise and data imbalance on the mapping of health status relationship. Performance evaluation of the MMRMCNN is conducted using a measured dataset, the PU bearing dataset, and a rotor dataset. We found that the fault recognition accuracy of the proposed method exceeds 97.79 %, and the accuracy of fault recognition under noisy background and unbalanced data conditions is also above 94.81 % and 94.72 %, respectively. This demonstrate the superior fault recognition capabilities of the proposed method in scenarios characterized by strong background noise and data imbalance. Overall, the results attest to the exceptional performances of the developed MMRMCNN in fault recognition under challenging conditions, underscoring its potential in advancing the field of in Telligent fault diagnosis for rotary machines.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 1-21"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prescribed performance ADRC position stabilization control of gangway tips under velocity constrains

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.01.048

Meng Li , Jialu Du , Dayu Xu

Consider an offshore gangway with vessel-motion-induced disturbances, dynamic uncertainties and friction force uncertainties under velocity constrains of the gangway tip (GT). A prescribed-time extended state observer (PTESO) is innovatively constructed by means of an error mapping function with respect to a prescribed performance function (PPF) where the problem that the gains of the existing PTESOs approach to infinity at a prescribed time is solved. The constructed PTESO with prescribed-time convergent estimation errors can provide the estimates of the GT motion states and the gangway total disturbances, respectively. Further, a new barrier function with respect to the PPF, the gangway tip position stabilization (GTPS) error and the GT velocity is proposed to handle the velocity constrains of the GT. Based on the above, a prescribed-performance control law for the GTPS is developed such that the GTPS errors converge to a prescribed tolerance steady-state error band in a prescribed settling time. Therein, our proposed barrier function is used as the component of the gain of the developed control law, unlike in the existing literatures used as the barrier Lyapunov functions, such that the developed control law is easy to implement. Simulation results exhibit that the GTPS errors under our developed control law are decreased by 16 % and 19 % in two cases with different sea states and model parameters, respectively.

{"title":"Prescribed performance ADRC position stabilization control of gangway tips under velocity constrains","authors":"Meng Li , Jialu Du , Dayu Xu","doi":"10.1016/j.isatra.2025.01.048","DOIUrl":"10.1016/j.isatra.2025.01.048","url":null,"abstract":"<div><div>Consider an offshore gangway with vessel-motion-induced disturbances, dynamic uncertainties and friction force uncertainties under velocity constrains of the gangway tip (GT). A prescribed-time extended state observer (PTESO) is innovatively constructed by means of an error mapping function with respect to a prescribed performance function (PPF) where the problem that the gains of the existing PTESOs approach to infinity at a prescribed time is solved. The constructed PTESO with prescribed-time convergent estimation errors can provide the estimates of the GT motion states and the gangway total disturbances, respectively. Further, a new barrier function with respect to the PPF, the gangway tip position stabilization (GTPS) error and the GT velocity is proposed to handle the velocity constrains of the GT. Based on the above, a prescribed-performance control law for the GTPS is developed such that the GTPS errors converge to a prescribed tolerance steady-state error band in a prescribed settling time. Therein, our proposed barrier function is used as the component of the gain of the developed control law, unlike in the existing literatures used as the barrier Lyapunov functions, such that the developed control law is easy to implement. Simulation results exhibit that the GTPS errors under our developed control law are decreased by 16 % and 19 % in two cases with different sea states and model parameters, respectively.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 216-225"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

External torque estimation based P+Damping control for bilateral teleoperation systems

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.01.049

Baorui Jing, Jing Na, Hao Duan, Yantian Li, Xian Wang, Yingbo Huang, Guang Li

This paper presents a P+Damping control with gravity compensation for bilateral teleoperation systems, where the effects of time delays can be effectively addressed, and the motion synchronization and the transparency can be guaranteed. To further eliminate the direct measurement of human and environmental torques to save sensor installation space and reduce the hardware cost, a constructive unknown torque estimator is also proposed by tailoring the idea of unknown system dynamics estimator (USDE). This torque estimator with low-pass filter operations eliminates the requirement of joint acceleration and has only one tuning parameter, while it enables accurate estimation of external torques of both the local and remote manipulators. Nevertheless, this USDE-based torque estimator can be incorporated into the P+Damping control to simultaneously enhance transparency during the contact motion and ensure the synchronization of both position and velocity. Rigorous theoretical analyses are carried out to prove the stability and the claimed performances. Finally, the effectiveness of the proposed methods is demonstrated through simulation and experimental results.

本文提出了一种带有重力补偿的 P+Damping 控制，可用于双边远程操纵系统，有效解决了时间延迟的影响，保证了运动的同步性和透明性。为了进一步消除对人体和环境扭矩的直接测量，以节省传感器安装空间并降低硬件成本，本文还通过调整未知系统动力学估计器（USDE）的思想，提出了一种建设性的未知扭矩估计器。这种扭矩估算器采用低通滤波器操作，无需关节加速度，只有一个调整参数，同时还能准确估算本地和远程机械手的外部扭矩。不过，这种基于 USDE 的扭矩估算器可被纳入 P+Damping 控制中，以同时提高接触运动过程中的透明度，并确保位置和速度的同步。我们进行了严格的理论分析，以证明其稳定性和所宣称的性能。最后，通过仿真和实验结果证明了所提方法的有效性。

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引用次数: 0

Learning-based safety-guaranteed sliding mode affine formation maneuver control of quadrotors vulnerable to cyber-attacks

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.02.016

Muhammad Maaruf , Sami El-Ferik

This article studied the actor–critic learning scheme for safe leader–follower affine formation maneuver control of networked quadrotors under external disturbances, sensor deception attacks, and injection attacks on the actuators. The followers aim to track formation maneuvers such as scaling, shearing, translation, and rotation determined by the leaders. Motivated by increasing safety and performance requirements during formation maneuvering, the dynamic states of the quadrotors are constrained within prescribed safety constraints. A barrier Lyapunov function is employed to ensure that the safety constraints are not violated. Then, a distributed sliding mode control with actor–critic learning is formulated to facilitate accurate leader–follower affine formation maneuvers and reject malicious cyber-attack signals. The input gains that appear due to the attacks might corrupt the control direction. The Nussbaum gain function is coupled to the controller to tackle this problem. The actor system estimates the uncertain dynamics and malicious attack signals, while the critic network evaluates the control performance through the estimated long-term performance index. The overall stability of the closed-loop system has been proven to be bounded using the Lyapunov stability theorem. Finally, simulation results demonstrate the capability of the presented control method.

{"title":"Learning-based safety-guaranteed sliding mode affine formation maneuver control of quadrotors vulnerable to cyber-attacks","authors":"Muhammad Maaruf , Sami El-Ferik","doi":"10.1016/j.isatra.2025.02.016","DOIUrl":"10.1016/j.isatra.2025.02.016","url":null,"abstract":"<div><div>This article studied the actor–critic learning scheme for safe leader–follower affine formation maneuver control of networked quadrotors under external disturbances, sensor deception attacks, and injection attacks on the actuators. The followers aim to track formation maneuvers such as scaling, shearing, translation, and rotation determined by the leaders. Motivated by increasing safety and performance requirements during formation maneuvering, the dynamic states of the quadrotors are constrained within prescribed safety constraints. A barrier Lyapunov function is employed to ensure that the safety constraints are not violated. Then, a distributed sliding mode control with actor–critic learning is formulated to facilitate accurate leader–follower affine formation maneuvers and reject malicious cyber-attack signals. The input gains that appear due to the attacks might corrupt the control direction. The Nussbaum gain function is coupled to the controller to tackle this problem. The actor system estimates the uncertain dynamics and malicious attack signals, while the critic network evaluates the control performance through the estimated long-term performance index. The overall stability of the closed-loop system has been proven to be bounded using the Lyapunov stability theorem. Finally, simulation results demonstrate the capability of the presented control method.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 66-79"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust nonlinear predefined-time cooperative line-of-sight guidance law for active defense in three-body confrontation

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.01.039

Qiang Qi , Chi Peng , Hanwen Zhang , Xiangwei Bu , Jianjun Ma

Conventional active guidance laws can achieve asymptotic or finite-time convergence of the line-of-sight (LOS) angle error. However, the convergence time is relatively conservative. In this article, we propose predefined-time (PT) sliding-mode (SM) convergence active defense guidance laws. Firstly, the proposed guidance law is designed based on a nonlinear LOS guidance framework, eliminating the assumption of small flight-path angles. Then, robust PT control theory is employed to achieve PT convergence of the SM surface. Moreover, two levels of cooperation between the target and the defender are introduced to enhance guidance performance. To minimize maximum overload, static optimization is incorporated in two-way cooperation by a performance index. The obtained guidance law does not require an observer or time-to-go estimation as compared to previous work. Additionally, our method can be applied not only in planar plane but also extended directly to three-dimensional geometry. Finally, we validate the effectiveness of our proposed strategies through comparative simulations.

{"title":"Robust nonlinear predefined-time cooperative line-of-sight guidance law for active defense in three-body confrontation","authors":"Qiang Qi , Chi Peng , Hanwen Zhang , Xiangwei Bu , Jianjun Ma","doi":"10.1016/j.isatra.2025.01.039","DOIUrl":"10.1016/j.isatra.2025.01.039","url":null,"abstract":"<div><div>Conventional active guidance laws can achieve asymptotic or finite-time convergence of the line-of-sight (LOS) angle error. However, the convergence time is relatively conservative. In this article, we propose predefined-time (PT) sliding-mode (SM) convergence active defense guidance laws. Firstly, the proposed guidance law is designed based on a nonlinear LOS guidance framework, eliminating the assumption of small flight-path angles. Then, robust PT control theory is employed to achieve PT convergence of the SM surface. Moreover, two levels of cooperation between the target and the defender are introduced to enhance guidance performance. To minimize maximum overload, static optimization is incorporated in two-way cooperation by a performance index. The obtained guidance law does not require an observer or time-to-go estimation as compared to previous work. Additionally, our method can be applied not only in planar plane but also extended directly to three-dimensional geometry. Finally, we validate the effectiveness of our proposed strategies through comparative simulations.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 181-190"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive self-triggered distributed filtering over sensor networks with partially unknown probabilities

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-04-01 DOI: 10.1016/j.isatra.2025.02.006

Zhongqi Li, Fengzeng Zhu, Ancai Zhang, Xiao Liang

The current work presents a distributed estimation approach with a topology-switching structure and introduces an adaptive self-triggered strategy (ASTS) to minimize energy consumption during inter-node communication. In the filter design, the network’s communication topology is modeled as a time-varying process, with switching governed by a homogeneous Markov chain and a probabilistic transition matrix containing partially unknown data. Filter design feasibility is verified using Lyapunov stability theory and linear matrix inequality (LMI) method, which are used to determine the filter parameters. Numerical simulation and practical experiment with a continuous stirred tank reactor validate the proposed approach.

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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