The hardness of the coal seam significantly influences the relationship between feeding speed and resistance at the drill bit. Due to the compressive deformation of the drill string, maintaining a stable feeding speed during drilling remains challenging. In this article, we propose a robust H-infinity dynamic output feedback controller, formulated to explicitly address uncertainties in formation hardness, thereby ensuring stable feeding speed and improved dynamic performance of the feed system under diverse drilling conditions. First, we develop a bitrock interaction model that incorporates the uncertainties in formation hardness and the rock-breaking threshold, which are key factors affecting drilling performance. By integrating this model with a finite element representation of the drill string, the feeding system is recast as a norm-bounded uncertain system. On-site data is utilized to replicate drilling conditions and validate the accuracy of the model. Subsequently, considering the uncertain parameters and industrial performance requirements, a dynamic output feedback controller is designed using robust H-infinity optimization with tailored weighting functions. This controller maintains stable feeding speed across varying formation hardness, effectively suppressing the impact of hardness fluctuations by attenuating resonance peaks. Both simulation and field experiments confirm that the proposed controller substantially reduces feeding speed fluctuations and achieves the desired robustness and control performance.
{"title":"Robust H-Infinity Control of Feeding Speed in Coal Seam Drilling Process With Uncertain Hardness","authors":"Luefeng Chen;Xiao Liu;Min Wu;Kaoru Hirota;Witold Pedrycz","doi":"10.1109/TCST.2025.3615663","DOIUrl":"https://doi.org/10.1109/TCST.2025.3615663","url":null,"abstract":"The hardness of the coal seam significantly influences the relationship between feeding speed and resistance at the drill bit. Due to the compressive deformation of the drill string, maintaining a stable feeding speed during drilling remains challenging. In this article, we propose a robust H-infinity dynamic output feedback controller, formulated to explicitly address uncertainties in formation hardness, thereby ensuring stable feeding speed and improved dynamic performance of the feed system under diverse drilling conditions. First, we develop a bitrock interaction model that incorporates the uncertainties in formation hardness and the rock-breaking threshold, which are key factors affecting drilling performance. By integrating this model with a finite element representation of the drill string, the feeding system is recast as a norm-bounded uncertain system. On-site data is utilized to replicate drilling conditions and validate the accuracy of the model. Subsequently, considering the uncertain parameters and industrial performance requirements, a dynamic output feedback controller is designed using robust H-infinity optimization with tailored weighting functions. This controller maintains stable feeding speed across varying formation hardness, effectively suppressing the impact of hardness fluctuations by attenuating resonance peaks. Both simulation and field experiments confirm that the proposed controller substantially reduces feeding speed fluctuations and achieves the desired robustness and control performance.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 1","pages":"381-394"},"PeriodicalIF":3.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915547","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}
In this article, we address the problem of online detection of interturn short-circuit faults (ITSCFs) that occur in interior- and surface-mounted permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: 1) a very simple linear observer and 2) a generalized parameter estimation-based observer, that incorporates a high performance estimator—with both observers detecting the short-circuit current and the fault intensity. Although the first solution guarantees the detection of the fault exponentially fast, the rate of convergence is fully determined by the motor parameters that, in some cases, may be too slow. The second observer, on the other hand, ensures finite convergence time (FCT) under the weakest assumption of interval excitation (IE). To make the observers adaptive, we develop a parameter estimator that, in the case of surface-mounted motors, estimates online (exponentially fast) the resistance and inductance of the motor. It should be underscored that, in contrast with existing observers (including the widely popular Kalman filter) that provide indirect information of the fault current, our observers provide an explicit one—namely the amplitude of the fault current. An additional advantage of the observers is that they do not require the knowledge of the motor currents, making them insensitive to current measurement noise. The performance of both observers, in their linear and generalized parameter estimation-based versions, is illustrated with realistic simulation studies.
{"title":"Interturn Fault Detection in PMSMs: Two Adaptive Observer-Based Noise Insensitive Solutions","authors":"Romeo Ortega;Alexey Bobtsov;Leyan Fang;Oscar Texis-Loaiza;Johannes Schiffer","doi":"10.1109/TCST.2025.3612450","DOIUrl":"https://doi.org/10.1109/TCST.2025.3612450","url":null,"abstract":"In this article, we address the problem of online detection of interturn short-circuit faults (ITSCFs) that occur in interior- and surface-mounted permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: 1) a very simple linear observer and 2) a generalized parameter estimation-based observer, that incorporates a high performance estimator—with both observers detecting the short-circuit current and the fault intensity. Although the first solution guarantees the detection of the fault exponentially fast, the rate of convergence is fully determined by the motor parameters that, in some cases, may be too slow. The second observer, on the other hand, ensures <italic>finite convergence time</i> (FCT) under the weakest assumption of interval excitation (IE). To make the observers <italic>adaptive</i>, we develop a parameter estimator that, in the case of surface-mounted motors, estimates online (exponentially fast) the resistance and inductance of the motor. It should be underscored that, in contrast with existing observers (including the widely popular Kalman filter) that provide <italic>indirect</i> information of the fault current, our observers provide an <italic>explicit</i> one—namely the amplitude of the fault current. An additional advantage of the observers is that they <italic>do not require</i> the knowledge of the motor currents, making them insensitive to current measurement noise. The performance of both observers, in their linear and generalized parameter estimation-based versions, is illustrated with realistic simulation studies.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 1","pages":"369-380"},"PeriodicalIF":3.9,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915597","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}
Pub Date : 2025-10-06DOI: 10.1109/TCST.2025.3613760
Yu Shan;Xiangpeng Xie;Yang Liu
This article focuses on the security control problem of a class of cyber-physical systems (CPSs) with external disturbances. There are two main objectives of this article. First, a covert-switching-based (CSB) attack mechanism is proposed from the attacker’s point of view to enhance the expected destructiveness and flexibility. Different from most existing attack models, the attack mechanism proposed in this article can dynamically adjust the duration of different types of attacks by changing the scheduling parameters and can expect greater system performance degradation. Second, in order to correspond to the proposed CSB attack mechanism and reduce the negative impact of the attacks, a polynomial parameter-dependent switching multi-instantaneous gain-scheduling (SMIGS) control law based on the normalized fuzzy weighted membership degrees (NFWMDs) of the current and past moments is designed from the defender’s point of view. Then, with the help of the Lyapunov function, sufficient conditions for mean exponential stability are successfully established to ensure the $H_{infty }$ performance of the error system. Finally, the progressiveness of the proposed strategy is verified by hardware-in-the-loop (HIL) simulation.
{"title":"Defense of Cyber-Physical Systems Against Covert-Switching-Based Attacks: A Switching Multi-Instantaneous Gain-Scheduling Mechanism","authors":"Yu Shan;Xiangpeng Xie;Yang Liu","doi":"10.1109/TCST.2025.3613760","DOIUrl":"https://doi.org/10.1109/TCST.2025.3613760","url":null,"abstract":"This article focuses on the security control problem of a class of cyber-physical systems (CPSs) with external disturbances. There are two main objectives of this article. First, a covert-switching-based (CSB) attack mechanism is proposed from the attacker’s point of view to enhance the expected destructiveness and flexibility. Different from most existing attack models, the attack mechanism proposed in this article can dynamically adjust the duration of different types of attacks by changing the scheduling parameters and can expect greater system performance degradation. Second, in order to correspond to the proposed CSB attack mechanism and reduce the negative impact of the attacks, a polynomial parameter-dependent switching multi-instantaneous gain-scheduling (SMIGS) control law based on the normalized fuzzy weighted membership degrees (NFWMDs) of the current and past moments is designed from the defender’s point of view. Then, with the help of the Lyapunov function, sufficient conditions for mean exponential stability are successfully established to ensure the <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> performance of the error system. Finally, the progressiveness of the proposed strategy is verified by hardware-in-the-loop (HIL) simulation.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 1","pages":"434-445"},"PeriodicalIF":3.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915609","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}
Pub Date : 2025-10-06DOI: 10.1109/TCST.2025.3613899
Youqing Wang;Tongze Hou;Mingliang Cui;Tao Chen;Xin Ma
Traditional multivariate statistical process monitoring algorithms focus on whether measurements are significantly shifted compared with the training data, but lack further analysis of the monitoring results. This results in frequent alarm triggering for process variations that do not require urgent operator attention, such as operating condition deviations, faults that are unrelated to key performance indicators (KPI), and faults that are compensated by the closed-loop system feedback mechanism. Machine downtime for every alarm leads to high economic losses for the plant. Therefore, it is important to perform Fault Risk analysis to identify security threats from process variations. A risk-oriented approach should be able to determine whether faults are associated with safety or quality risks, thereby reducing overhaul costs and increasing economic efficiency. In this study, a Fault Risk analysis framework is proposed for nonlinear dynamic processes based on a kernel dynamic regression (KDR) model. The framework consists of two algorithms: one is KDR for detecting process faults, and the other is KPI-related KDR (KPI-KDR) for detecting faults affecting product quality. The proposed approaches provide more reasonable and interpretable dynamic and static subspace decomposition, which facilitates further analysis of the monitoring results. First, the KDR concurrently detects normal operating condition deviations and process faults. Then, the KPI-KDR analyzes whether faults can be compensated by feedback mechanisms. Finally, a closed-loop continuous stirred tank reactor and real catalytic cracking unit data are used to validate the effective performance of the proposed algorithms.
{"title":"Monitoring and Fault Risk Analysis for Nonlinear Dynamic Processes Based on Kernel Dynamic Regression Model","authors":"Youqing Wang;Tongze Hou;Mingliang Cui;Tao Chen;Xin Ma","doi":"10.1109/TCST.2025.3613899","DOIUrl":"https://doi.org/10.1109/TCST.2025.3613899","url":null,"abstract":"Traditional multivariate statistical process monitoring algorithms focus on whether measurements are significantly shifted compared with the training data, but lack further analysis of the monitoring results. This results in frequent alarm triggering for process variations that do not require urgent operator attention, such as operating condition deviations, faults that are unrelated to key performance indicators (KPI), and faults that are compensated by the closed-loop system feedback mechanism. Machine downtime for every alarm leads to high economic losses for the plant. Therefore, it is important to perform Fault Risk analysis to identify security threats from process variations. A risk-oriented approach should be able to determine whether faults are associated with safety or quality risks, thereby reducing overhaul costs and increasing economic efficiency. In this study, a Fault Risk analysis framework is proposed for nonlinear dynamic processes based on a kernel dynamic regression (KDR) model. The framework consists of two algorithms: one is KDR for detecting process faults, and the other is KPI-related KDR (KPI-KDR) for detecting faults affecting product quality. The proposed approaches provide more reasonable and interpretable dynamic and static subspace decomposition, which facilitates further analysis of the monitoring results. First, the KDR concurrently detects normal operating condition deviations and process faults. Then, the KPI-KDR analyzes whether faults can be compensated by feedback mechanisms. Finally, a closed-loop continuous stirred tank reactor and real catalytic cracking unit data are used to validate the effective performance of the proposed algorithms.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 1","pages":"411-425"},"PeriodicalIF":3.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915570","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}
Pub Date : 2025-10-03DOI: 10.1109/TCST.2025.3587096
Máté B. Vizi;Gábor Orosz;Dénes Takács;Gábor Stépán
The steering control of an autonomous unicycle is considered. The underlying dynamical model of a single rolling wheel is discussed regarding the steady-state motions and their stability. The unicycle model is introduced as the simplest possible extension of the rolling wheel, where the location of the center of gravity is controlled. With the help of the Appellian approach, a state-space representation of the controlled nonholonomic system is built in a way that the most compact nonlinear equations of motion are constructed. Based on controllability analysis, feedback controllers are designed that successfully carry out lane changing and turning maneuvers. The behavior of the closed-loop system is demonstrated by numerical simulations.
{"title":"Steering Control of an Autonomous Unicycle","authors":"Máté B. Vizi;Gábor Orosz;Dénes Takács;Gábor Stépán","doi":"10.1109/TCST.2025.3587096","DOIUrl":"https://doi.org/10.1109/TCST.2025.3587096","url":null,"abstract":"The steering control of an autonomous unicycle is considered. The underlying dynamical model of a single rolling wheel is discussed regarding the steady-state motions and their stability. The unicycle model is introduced as the simplest possible extension of the rolling wheel, where the location of the center of gravity is controlled. With the help of the Appellian approach, a state-space representation of the controlled nonholonomic system is built in a way that the most compact nonlinear equations of motion are constructed. Based on controllability analysis, feedback controllers are designed that successfully carry out lane changing and turning maneuvers. The behavior of the closed-loop system is demonstrated by numerical simulations.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 6","pages":"2393-2409"},"PeriodicalIF":3.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145341073","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}
Pub Date : 2025-10-01DOI: 10.1109/TCST.2025.3610975
Luis Romero-Ben;Paul Irofti;Florin Stoican;Vicenç Puig
Leakage in water systems causes significant daily losses, degraded service, increased costs, and environmental problems. Efficient leak localization is crucial to minimizing these impacts. Most leak management methods rely solely on pressure sensors due to their reduced cost and ease of installation, potentially missing other sources of valuable data. This article proposes a data-driven hydraulic state estimation methodology based on a dual unscented Kalman filter (UKF) approach, which enables the fusion of pressure, flow, and demand measurements. In this way, the presented method enhances the estimation of both nodal hydraulic heads, critical in localization tasks, and pipe flows, useful for operational purposes. The strategy is evaluated in well-known open source case studies, namely Modena and L-TOWN. In terms of interpolation accuracy, the average performance in both scenarios shows a ~52% error reduction in pressure estimation if comparing with methods only using pressure sensors, and a ~24% error reduction in flow estimation if comparing with a nondual UKF implementation. Moreover, leak localization results in L-TOWN show an average distance error reduction of ~14%, with impressive results in a multileak scenario where a ~68% reduction is reached.
{"title":"Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization","authors":"Luis Romero-Ben;Paul Irofti;Florin Stoican;Vicenç Puig","doi":"10.1109/TCST.2025.3610975","DOIUrl":"https://doi.org/10.1109/TCST.2025.3610975","url":null,"abstract":"Leakage in water systems causes significant daily losses, degraded service, increased costs, and environmental problems. Efficient leak localization is crucial to minimizing these impacts. Most leak management methods rely solely on pressure sensors due to their reduced cost and ease of installation, potentially missing other sources of valuable data. This article proposes a data-driven hydraulic state estimation methodology based on a dual unscented Kalman filter (UKF) approach, which enables the fusion of pressure, flow, and demand measurements. In this way, the presented method enhances the estimation of both nodal hydraulic heads, critical in localization tasks, and pipe flows, useful for operational purposes. The strategy is evaluated in well-known open source case studies, namely Modena and L-TOWN. In terms of interpolation accuracy, the average performance in both scenarios shows a ~52% error reduction in pressure estimation if comparing with methods only using pressure sensors, and a ~24% error reduction in flow estimation if comparing with a nondual UKF implementation. Moreover, leak localization results in L-TOWN show an average distance error reduction of ~14%, with impressive results in a multileak scenario where a ~68% reduction is reached.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 1","pages":"343-354"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11185271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-09-11DOI: 10.1109/TCST.2025.3590268
Henrik Hose;Johannes Köhler;Melanie N. Zeilinger;Sebastian Trimpe
Model predictive control (MPC) achieves stability and constraint satisfaction for general nonlinear systems but requires computationally expensive online optimization. This brief studies approximations of such MPC controllers via neural networks (NNs) to achieve fast online evaluation. We propose safety augmentation that yields deterministic guarantees for convergence and constraint satisfaction despite approximation inaccuracies. We approximate the entire input sequence of the MPC with NNs, which allows us to verify online if it is a feasible solution to the MPC problem. We replace the NN solution by a safe candidate based on standard MPC techniques whenever it is infeasible or has worse cost. Our method requires a single evaluation of the NN and forward integration of the input sequence online, which is fast to compute on resource-constrained systems, typically within 0.2 ms. The proposed control framework is illustrated using three numerical nonlinear MPC benchmarks of different complexities, demonstrating computational speedups that are orders of magnitude higher than online optimization. In the examples, we achieve deterministic safety through the safety-augmented NNs, where a naive NN implementation fails.
{"title":"Approximate Nonlinear Model Predictive Control With Safety-Augmented Neural Networks","authors":"Henrik Hose;Johannes Köhler;Melanie N. Zeilinger;Sebastian Trimpe","doi":"10.1109/TCST.2025.3590268","DOIUrl":"https://doi.org/10.1109/TCST.2025.3590268","url":null,"abstract":"Model predictive control (MPC) achieves stability and constraint satisfaction for general nonlinear systems but requires computationally expensive online optimization. This brief studies approximations of such MPC controllers via neural networks (NNs) to achieve fast online evaluation. We propose safety augmentation that yields deterministic guarantees for convergence and constraint satisfaction despite approximation inaccuracies. We approximate the entire input sequence of the MPC with NNs, which allows us to verify online if it is a feasible solution to the MPC problem. We replace the NN solution by a safe candidate based on standard MPC techniques whenever it is infeasible or has worse cost. Our method requires a single evaluation of the NN and forward integration of the input sequence online, which is fast to compute on resource-constrained systems, typically within 0.2 ms. The proposed control framework is illustrated using three numerical nonlinear MPC benchmarks of different complexities, demonstrating computational speedups that are orders of magnitude higher than online optimization. In the examples, we achieve deterministic safety through the safety-augmented NNs, where a naive NN implementation fails.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 6","pages":"2490-2497"},"PeriodicalIF":3.9,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339718","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}
Pub Date : 2025-09-08DOI: 10.1109/TCST.2025.3603205
Yasaman Masoudi;S. M. Mahdi Alavi;Nasser L. Azad
Electrochemical impedance spectroscopy (EIS) is a prevalent technique for battery characterization and testing. Despite recent advancements, EIS optimization remains a challenge faced by conventional EIS sampling techniques. One such example is the redundancy of samples. For instance, additional excitation frequencies are used to identify the parameters of a given equivalent circuit model (ECM), while this can be done with fewer excitation frequencies from a geometrical standpoint. In this brief, fundamentals of geometric identifiability of EIS ECMs are discussed and showcased for a framework formulated based on Fisher information matrix (FIM) optimization sampling. By using the geometric identifiability analysis, it is shown that the classic FIM EIS, optimizing the sensitivity of likelihood function to all the unknown parameters, leads to redundant samples. A geometric FIM EIS is then proposed, which reduces the number of sampling regions without compromising the estimation accuracy. It is yet shown that both the classic and the proposed geometric FIM EIS methods are suboptimal with respect to the geometric identifiability analysis. Both ordinary-order and factional-order ECMs are discussed, and simulation results are provided and compared against that of a conventional EIS implementation with uniform sampling.
{"title":"Making the Case for Geometric Identifiability in Electrochemical Impedance Spectroscopy","authors":"Yasaman Masoudi;S. M. Mahdi Alavi;Nasser L. Azad","doi":"10.1109/TCST.2025.3603205","DOIUrl":"https://doi.org/10.1109/TCST.2025.3603205","url":null,"abstract":"Electrochemical impedance spectroscopy (EIS) is a prevalent technique for battery characterization and testing. Despite recent advancements, EIS optimization remains a challenge faced by conventional EIS sampling techniques. One such example is the redundancy of samples. For instance, additional excitation frequencies are used to identify the parameters of a given equivalent circuit model (ECM), while this can be done with fewer excitation frequencies from a geometrical standpoint. In this brief, fundamentals of geometric identifiability of EIS ECMs are discussed and showcased for a framework formulated based on Fisher information matrix (FIM) optimization sampling. By using the geometric identifiability analysis, it is shown that the classic FIM EIS, optimizing the sensitivity of likelihood function to all the unknown parameters, leads to redundant samples. A geometric FIM EIS is then proposed, which reduces the number of sampling regions without compromising the estimation accuracy. It is yet shown that both the classic and the proposed geometric FIM EIS methods are suboptimal with respect to the geometric identifiability analysis. Both ordinary-order and factional-order ECMs are discussed, and simulation results are provided and compared against that of a conventional EIS implementation with uniform sampling.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"34 1","pages":"524-530"},"PeriodicalIF":3.9,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915620","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}
Pub Date : 2025-09-05DOI: 10.1109/TCST.2025.3593237
Karan Mahesh;Tyler M. Paine;Max L. Greene;Nicholas Rober;Steven Lee;Sildomar T. Monteiro;Anuradha Annaswamy;Michael R. Benjamin;Jonathan P. How
Marine robots must maintain precise control and ensure safety during tasks such as navigating narrow waterways, even when they encounter unpredictable disturbances that impact performance. Designing algorithms for uncrewed surface vehicles (USVs) requires accounting for these disturbances to control the vehicle and ensure it avoids obstacles. While adaptive control has addressed USV control challenges, real-world applications are limited, and certifying USV safety amidst unexpected disturbances remains difficult. To tackle control issues, we employ a model reference adaptive controller (MRAC) to stabilize the USV along a desired trajectory. For safety certification, we developed a reachability module with a moving horizon estimator (MHE) to estimate disturbances affecting the USV. This estimate is propagated through a forward reachable set calculation, predicting future states and enabling real-time safety certification. We tested our safe autonomy pipeline on a Clearpath Heron USV in the Charles River, near MIT. Our experiments demonstrated that the USV’s MRAC controller and reachability module could adapt to disturbances like thruster failures and drag forces. The MRAC controller outperformed a proportional–integral–derivative (PID) baseline, showing a 45%–81% reduction in position root mean squared error. In addition, the reachability module provided real-time safety certification, ensuring the USV’s safety. We further validated our pipeline’s effectiveness in underway replenishment and canal scenarios.
{"title":"Safe Autonomy for Uncrewed Surface Vehicles Using Adaptive Control and Reachability Analysis","authors":"Karan Mahesh;Tyler M. Paine;Max L. Greene;Nicholas Rober;Steven Lee;Sildomar T. Monteiro;Anuradha Annaswamy;Michael R. Benjamin;Jonathan P. How","doi":"10.1109/TCST.2025.3593237","DOIUrl":"https://doi.org/10.1109/TCST.2025.3593237","url":null,"abstract":"Marine robots must maintain precise control and ensure safety during tasks such as navigating narrow waterways, even when they encounter unpredictable disturbances that impact performance. Designing algorithms for uncrewed surface vehicles (USVs) requires accounting for these disturbances to control the vehicle and ensure it avoids obstacles. While adaptive control has addressed USV control challenges, real-world applications are limited, and certifying USV safety amidst unexpected disturbances remains difficult. To tackle control issues, we employ a model reference adaptive controller (MRAC) to stabilize the USV along a desired trajectory. For safety certification, we developed a reachability module with a moving horizon estimator (MHE) to estimate disturbances affecting the USV. This estimate is propagated through a forward reachable set calculation, predicting future states and enabling real-time safety certification. We tested our safe autonomy pipeline on a Clearpath Heron USV in the Charles River, near MIT. Our experiments demonstrated that the USV’s MRAC controller and reachability module could adapt to disturbances like thruster failures and drag forces. The MRAC controller outperformed a proportional–integral–derivative (PID) baseline, showing a 45%–81% reduction in position root mean squared error. In addition, the reachability module provided real-time safety certification, ensuring the USV’s safety. We further validated our pipeline’s effectiveness in underway replenishment and canal scenarios.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 6","pages":"2334-2349"},"PeriodicalIF":3.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11152616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2025-09-03DOI: 10.1109/TCST.2025.3601191
Mizraim Martinez-Lopez;Javier Moreno-Valenzuela;Jerónimo Moyrón;Mahdieh S. Sadabadi
Trajectory tracking in dc–dc buck converters is critical for applications requiring precise and dynamic voltage control. Integral-action controllers, commonly used for voltage regulation and trajectory tracking, often fail to account for input constraints, leading to integrator windup and potential instability. To address these challenges, we propose a novel adaptive nonlinear proportional-derivative plus limited integrator anti-windup (PD+LIAW) controller, which, to the best of our knowledge, represents the first solution to integrator windup in trajectory tracking for buck converters. The proposed controller ensures precise tracking of time-varying References, effectively mitigates integrator windup, and delivers robust performance under input saturation and parameter variations. To the best of our knowledge, this work presents the first rigorous Lyapunov-based stability analysis of the LIAW method for trajectory tracking, incorporating adaptation to parameter uncertainties. The experimental results demonstrate the superior performance of the proposed algorithm compared to conventional linear proportional–integral–derivative (PID) controllers and state-of-the-art methods, showcasing improved trajectory tracking, enhanced robustness, and effective mitigation of windup-induced instabilities.
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