Pub Date : 2025-02-27DOI: 10.1109/TCST.2025.3542215
M. C. Thompson;C. T. Freeman;N. O’Brien;A.-M. Hughes;R. Marchbanks;A. Birch
In U.K. 60 000 people have a brain tumor and typically are unaware of its presence until symptoms occur. Currently, there is no mass screening available due to limitations in diagnostic techniques. Measurement of intracranial pressure (ICP) [via tympanic membrane displacement (TMD)] is a potential low-cost, accessible solution; however, pressure fluctuations degrade its accuracy. This article solves the problem by assisting participants to precisely track airway pressure profiles. This stabilizes intrathoracic pressure, significantly reducing the fluctuations and enabling accurate diagnosis of ICP. This article develops and evaluates the first model of lung pressure tracking to embed volitional control action. A clinically feasible identification approach is then derived, together with a novel model predictive control (MPC) framework, embedding a valve control subsystem. Results with ten participants confirm that tracking is improved by an average of 22%.
{"title":"Model Predictive Valve Control to Assist Lung Pressure Profile Tracking","authors":"M. C. Thompson;C. T. Freeman;N. O’Brien;A.-M. Hughes;R. Marchbanks;A. Birch","doi":"10.1109/TCST.2025.3542215","DOIUrl":"https://doi.org/10.1109/TCST.2025.3542215","url":null,"abstract":"In U.K. 60 000 people have a brain tumor and typically are unaware of its presence until symptoms occur. Currently, there is no mass screening available due to limitations in diagnostic techniques. Measurement of intracranial pressure (ICP) [via tympanic membrane displacement (TMD)] is a potential low-cost, accessible solution; however, pressure fluctuations degrade its accuracy. This article solves the problem by assisting participants to precisely track airway pressure profiles. This stabilizes intrathoracic pressure, significantly reducing the fluctuations and enabling accurate diagnosis of ICP. This article develops and evaluates the first model of lung pressure tracking to embed volitional control action. A clinically feasible identification approach is then derived, together with a novel model predictive control (MPC) framework, embedding a valve control subsystem. Results with ten participants confirm that tracking is improved by an average of 22%.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1509-1520"},"PeriodicalIF":3.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891202","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 : 2025-02-25DOI: 10.1109/TCST.2025.3542212
Elias Klauser;Alireza Karimi
A novel approach for linear parameter-varying (LPV) controller synthesis for adaptive rejection of frequency-varying sinusoidal disturbances is proposed. Only the frequency response data of a linear time-invariant (LTI) multiple-input-multiple-output (MIMO) system are used to design the LPV controller that stabilizes the system for arbitrarily fast variation of the disturbance frequencies. Global stability is achieved thanks to the specific structure of the LPV controller and the use of integral quadratic constraints (IQCs) to represent the frequency variations. The LPV controller is designed by convex optimization in the frequency domain. A hybrid microvibration damping platform (MIVIDA) for space applications is considered for experimental validation of the proposed method. An LPV controller for rejection of unknown frequency-varying sinusoidal disturbances is designed and implemented on the real system. Experimental results demonstrate the effectiveness of the proposed approach in asymptotically rejecting disturbances and ensuring closed-loop stability against arbitrarily fast variations in disturbance frequencies.
{"title":"Data-Driven LPV Control for Harmonic Disturbance Rejection in a Hybrid Isolation Platform","authors":"Elias Klauser;Alireza Karimi","doi":"10.1109/TCST.2025.3542212","DOIUrl":"https://doi.org/10.1109/TCST.2025.3542212","url":null,"abstract":"A novel approach for linear parameter-varying (LPV) controller synthesis for adaptive rejection of frequency-varying sinusoidal disturbances is proposed. Only the frequency response data of a linear time-invariant (LTI) multiple-input-multiple-output (MIMO) system are used to design the LPV controller that stabilizes the system for arbitrarily fast variation of the disturbance frequencies. Global stability is achieved thanks to the specific structure of the LPV controller and the use of integral quadratic constraints (IQCs) to represent the frequency variations. The LPV controller is designed by convex optimization in the frequency domain. A hybrid microvibration damping platform (MIVIDA) for space applications is considered for experimental validation of the proposed method. An LPV controller for rejection of unknown frequency-varying sinusoidal disturbances is designed and implemented on the real system. Experimental results demonstrate the effectiveness of the proposed approach in asymptotically rejecting disturbances and ensuring closed-loop stability against arbitrarily fast variations in disturbance frequencies.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1532-1542"},"PeriodicalIF":3.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891241","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}
As a useful and efficient alternative to generic model-based control scheme, data-driven predictive control (DDPC) is subject to bias-variance tradeoff and is known to not perform desirably in face of uncertainty. Through the connection between direct data-driven control and subspace predictive control (SPC), we gain insight into the reason being the lack of causality as a main cause for their high variance of implicit prediction. In this brief, we derive a new causality-informed formulation of DDPC and its regularized form that balances between control cost minimization and implicit identification of a causal multistep predictor. Since the proposed causality-informed formulations only call for block-triangularization of a submatrix in the generic noncausal DDPC based on LQ factorization, our causality-informed formulation of DDPC enjoys computational efficiency. Its efficacy is investigated through numerical examples and application to model-free control of a simulated industrial heating furnace. Empirical results corroborate that the proposed method yields obvious performance improvement over existing formulations in handling stochastic noise and process nonlinearity.
{"title":"Causality-Informed Data-Driven Predictive Control","authors":"Malika Sader;Yibo Wang;Dexian Huang;Chao Shang;Biao Huang","doi":"10.1109/TCST.2025.3541179","DOIUrl":"https://doi.org/10.1109/TCST.2025.3541179","url":null,"abstract":"As a useful and efficient alternative to generic model-based control scheme, data-driven predictive control (DDPC) is subject to bias-variance tradeoff and is known to not perform desirably in face of uncertainty. Through the connection between direct data-driven control and subspace predictive control (SPC), we gain insight into the reason being the lack of causality as a main cause for their high variance of implicit prediction. In this brief, we derive a new causality-informed formulation of DDPC and its regularized form that balances between control cost minimization and implicit identification of a causal multistep predictor. Since the proposed causality-informed formulations only call for block-triangularization of a submatrix in the generic noncausal DDPC based on LQ factorization, our causality-informed formulation of DDPC enjoys computational efficiency. Its efficacy is investigated through numerical examples and application to model-free control of a simulated industrial heating furnace. Empirical results corroborate that the proposed method yields obvious performance improvement over existing formulations in handling stochastic noise and process nonlinearity.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1921-1928"},"PeriodicalIF":3.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891057","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 : 2025-02-17DOI: 10.1109/TCST.2025.3536215
Tamas G. Molnar;Suresh K. Kannan;James Cunningham;Kyle Dunlap;Kerianne L. Hobbs;Aaron D. Ames
Safety-critical failures often have fatal consequences in aerospace control. Control systems on aircraft, therefore, must ensure the strict satisfaction of safety constraints, preferably with formal guarantees of safe behavior. This article establishes the safety-critical control of fixed-wing aircraft in collision avoidance and geofencing tasks. A control framework is developed wherein a run-time assurance (RTA) system modulates the nominal flight controller of the aircraft whenever necessary to prevent it from colliding with other aircraft or crossing a boundary (geofence) in space. The RTA is formulated as a safety filter using control barrier functions (CBFs) with formal guarantees of safe behavior. CBFs are constructed and compared for a nonlinear kinematic fixed-wing aircraft model. The proposed CBF-based controllers showcase the capability of safely executing simultaneous collision avoidance and geofencing, as demonstrated by simulations on the kinematic model and a high-fidelity dynamical model.
{"title":"Collision Avoidance and Geofencing for Fixed-Wing Aircraft With Control Barrier Functions","authors":"Tamas G. Molnar;Suresh K. Kannan;James Cunningham;Kyle Dunlap;Kerianne L. Hobbs;Aaron D. Ames","doi":"10.1109/TCST.2025.3536215","DOIUrl":"https://doi.org/10.1109/TCST.2025.3536215","url":null,"abstract":"Safety-critical failures often have fatal consequences in aerospace control. Control systems on aircraft, therefore, must ensure the strict satisfaction of safety constraints, preferably with formal guarantees of safe behavior. This article establishes the safety-critical control of fixed-wing aircraft in collision avoidance and geofencing tasks. A control framework is developed wherein a run-time assurance (RTA) system modulates the nominal flight controller of the aircraft whenever necessary to prevent it from colliding with other aircraft or crossing a boundary (geofence) in space. The RTA is formulated as a safety filter using control barrier functions (CBFs) with formal guarantees of safe behavior. CBFs are constructed and compared for a nonlinear kinematic fixed-wing aircraft model. The proposed CBF-based controllers showcase the capability of safely executing simultaneous collision avoidance and geofencing, as demonstrated by simulations on the kinematic model and a high-fidelity dynamical model.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1493-1508"},"PeriodicalIF":3.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891197","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 : 2025-02-13DOI: 10.1109/TCST.2025.3537861
Albertus Johannes Malan;Joel Ferguson;Michele Cucuzzella;Jacquelien M. A. Scherpen;Sören Hohmann
In this article, we consider the problem of voltage stability in dc networks containing uncertain loads with non-monotone incremental impedances and where the steady-state power availability is restricted to a subset of the buses in the network. We propose controllers for powered buses that guarantee voltage regulation and output strictly equilibrium independent passivity (OS-EIP) of the controlled buses, while buses without power are equipped with controllers that dampen their transient behavior. The OS-EIP of a cluster containing both bus types is verified through a linear matrix inequality (LMI) condition, and the asymptotic stability of the overall microgrid with uncertain, non-monotone loads is ensured by interconnecting the OS-EIP clusters. By further using singular perturbation theory, we show that the OS-EIP property of the clusters is robust against certain network parameter and topology changes.
{"title":"Passivation of Clustered DC Microgrids With Non-Monotone Loads","authors":"Albertus Johannes Malan;Joel Ferguson;Michele Cucuzzella;Jacquelien M. A. Scherpen;Sören Hohmann","doi":"10.1109/TCST.2025.3537861","DOIUrl":"https://doi.org/10.1109/TCST.2025.3537861","url":null,"abstract":"In this article, we consider the problem of voltage stability in dc networks containing uncertain loads with non-monotone incremental impedances and where the steady-state power availability is restricted to a subset of the buses in the network. We propose controllers for powered buses that guarantee voltage regulation and output strictly equilibrium independent passivity (OS-EIP) of the controlled buses, while buses without power are equipped with controllers that dampen their transient behavior. The OS-EIP of a cluster containing both bus types is verified through a linear matrix inequality (LMI) condition, and the asymptotic stability of the overall microgrid with uncertain, non-monotone loads is ensured by interconnecting the OS-EIP clusters. By further using singular perturbation theory, we show that the OS-EIP property of the clusters is robust against certain network parameter and topology changes.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"1069-1084"},"PeriodicalIF":4.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883412","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 : 2025-02-13DOI: 10.1109/TCST.2025.3539219
William Jussiau;Fabrice Demourant;Colin Leclercq;Pierre Apkarian
This article presents a methodology for designing linear time-invariant (LTI) controllers to stabilize high-dimensional nonlinear oscillator systems with an unstable equilibrium and a periodic or quasiperiodic attractor. The proposed approach is hybrid, combining ideas from classic model- based methods and more recent data-based approaches. The model-based component is aimed at guaranteeing the stability of the closed-loop system near the equilibrium, which is formulated using Youla parametrization. The data-based component tackles the nonlinearity and high-dimensionality of the system by utilizing simulation data in conjunction with derivative-free optimization to design LTI controllers. The approach results in a collection of LTI controllers that not only asymptotically stabilize the system near its equilibrium, but also drive the system from the attractor to the stabilized equilibrium. The efficacy of the method is demonstrated on a challenging example of a high-dimensional nonlinear oscillator from fluid mechanics: the incompressible flow over a 2-D open cavity at $text {Re}=7500$ . Not only does it confirm the existence of simple LTI controllers stabilizing high-dimensional nonlinear dynamics in simulation, but it also shows the possibility of systematically finding solutions to this problem.
{"title":"Control of a Class of High-Dimensional Nonlinear Oscillators: Application to Flow Stabilization","authors":"William Jussiau;Fabrice Demourant;Colin Leclercq;Pierre Apkarian","doi":"10.1109/TCST.2025.3539219","DOIUrl":"https://doi.org/10.1109/TCST.2025.3539219","url":null,"abstract":"This article presents a methodology for designing linear time-invariant (LTI) controllers to stabilize high-dimensional nonlinear oscillator systems with an unstable equilibrium and a periodic or quasiperiodic attractor. The proposed approach is hybrid, combining ideas from classic model- based methods and more recent data-based approaches. The model-based component is aimed at guaranteeing the stability of the closed-loop system near the equilibrium, which is formulated using Youla parametrization. The data-based component tackles the nonlinearity and high-dimensionality of the system by utilizing simulation data in conjunction with derivative-free optimization to design LTI controllers. The approach results in a collection of LTI controllers that not only asymptotically stabilize the system near its equilibrium, but also drive the system from the attractor to the stabilized equilibrium. The efficacy of the method is demonstrated on a challenging example of a high-dimensional nonlinear oscillator from fluid mechanics: the incompressible flow over a 2-D open cavity at <inline-formula> <tex-math>$text {Re}=7500$ </tex-math></inline-formula>. Not only does it confirm the existence of simple LTI controllers stabilizing high-dimensional nonlinear dynamics in simulation, but it also shows the possibility of systematically finding solutions to this problem.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1521-1531"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891242","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}
This article studies the problem of data-driven combined longitudinal and lateral control of autonomous vehicles (AVs) such that the AV can stay within a safe but minimum distance from its leading vehicle and, at the same time, in the lane. Most of the existing methods for combined longitudinal and lateral control are either model-based or developed by purely data-driven methods such as reinforcement learning. Traditional model-based control approaches are insufficient to address the adaptive optimal control design issue for AVs in dynamically changing environments and are subject to model uncertainty. Moreover, the conventional reinforcement learning approaches require a large volume of data, and cannot guarantee the stability of the vehicle. These limitations are addressed by integrating the advanced control theory with reinforcement learning techniques. To be more specific, by utilizing adaptive dynamic programming (ADP) techniques and using the motion data collected from the vehicles, a policy iteration algorithm is proposed such that the control policy is iteratively optimized in the absence of the precise knowledge of the AV’s dynamical model. Furthermore, the stability of the AV is guaranteed with the control policy generated at each iteration of the algorithm. The efficiency of the proposed approach is validated by the integrated simulation of SUMO and CommonRoad.
{"title":"Data-Driven Combined Longitudinal and Lateral Control for the Car Following Problem","authors":"Leilei Cui;Sayan Chakraborty;Kaan Ozbay;Zhong-Ping Jiang","doi":"10.1109/TCST.2025.3539216","DOIUrl":"https://doi.org/10.1109/TCST.2025.3539216","url":null,"abstract":"This article studies the problem of data-driven combined longitudinal and lateral control of autonomous vehicles (AVs) such that the AV can stay within a safe but minimum distance from its leading vehicle and, at the same time, in the lane. Most of the existing methods for combined longitudinal and lateral control are either model-based or developed by purely data-driven methods such as reinforcement learning. Traditional model-based control approaches are insufficient to address the adaptive optimal control design issue for AVs in dynamically changing environments and are subject to model uncertainty. Moreover, the conventional reinforcement learning approaches require a large volume of data, and cannot guarantee the stability of the vehicle. These limitations are addressed by integrating the advanced control theory with reinforcement learning techniques. To be more specific, by utilizing adaptive dynamic programming (ADP) techniques and using the motion data collected from the vehicles, a policy iteration algorithm is proposed such that the control policy is iteratively optimized in the absence of the precise knowledge of the AV’s dynamical model. Furthermore, the stability of the AV is guaranteed with the control policy generated at each iteration of the algorithm. The efficiency of the proposed approach is validated by the integrated simulation of SUMO and CommonRoad.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"991-1005"},"PeriodicalIF":4.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883414","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 : 2025-02-11DOI: 10.1109/TCST.2025.3537820
Italo Aranda-Cetraro;Gustavo Pérez-Zuñiga
Most solutions for detecting, estimating, and localizing leaks in water networks rely on complex banks of Kalman filters (BKFs) or advanced stand-alone Kalman filter (KF) algorithms to account for the network’s model uncertainty, requiring extra hardware, extensive calibration, and maintenance. This study proposes a modulating function (MF) regressor based on a lumped model with pressure-flow boundary conditions to detect and localize a single leak in a water network. The uncertainty of the lumped model is reduced by adapting the MF regressor via a Lyapunov-based adaptive law. A real water network system (WNS) test bench was employed to validate the effectiveness of the proposed regressor. Initially, an experimental phase was conducted to identify and analyze the primary sources of uncertainty of the plant models concerning the test setup. Subsequently, the proposed leak detection, estimation, and localization algorithm was tested and compared with the robust adaptive unscented Kalman Filter (RAUKF), showing promising results.
{"title":"Adaptive Residual-Based Modulating Function Regressor for Decoupled Estimation of Leak Size and Localization in Uncertain Water Network Systems","authors":"Italo Aranda-Cetraro;Gustavo Pérez-Zuñiga","doi":"10.1109/TCST.2025.3537820","DOIUrl":"https://doi.org/10.1109/TCST.2025.3537820","url":null,"abstract":"Most solutions for detecting, estimating, and localizing leaks in water networks rely on complex banks of Kalman filters (BKFs) or advanced stand-alone Kalman filter (KF) algorithms to account for the network’s model uncertainty, requiring extra hardware, extensive calibration, and maintenance. This study proposes a modulating function (MF) regressor based on a lumped model with pressure-flow boundary conditions to detect and localize a single leak in a water network. The uncertainty of the lumped model is reduced by adapting the MF regressor via a Lyapunov-based adaptive law. A real water network system (WNS) test bench was employed to validate the effectiveness of the proposed regressor. Initially, an experimental phase was conducted to identify and analyze the primary sources of uncertainty of the plant models concerning the test setup. Subsequently, the proposed leak detection, estimation, and localization algorithm was tested and compared with the robust adaptive unscented Kalman Filter (RAUKF), showing promising results.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1479-1492"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891052","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 : 2025-02-06DOI: 10.1109/TCST.2025.3536203
Emilio Carfagna;Cristiano Maria Verrelli;Giovanni Migliazza;Fabio Bernardi;Emilio Lorenzani
In-depth performance analysis of stator flux observers (SFOs) is carried out, in the very low-speed range, for sensorless speed-controlled drives based on permanent magnet synchronous machines (PMSMs), in the presence of no idealities of the voltage source inverter (VSI) and uncertainties in the motor electrical parameters. The original contribution of this brief is twofold. First, it relies on the presentation of experiments, within this framework, which comparatively illustrate the closed-loop performance of: 1) a stator flux (open-loop) estimator with a low-pass filter (LPF), endowed with an additional phase shift and magnitude compensation based on the estimation of machine speed and 2) two adaptive observers constituting the most recent representatives of the class of the theoretically based contributions for PMSMs. While the former can achieve more satisfactory results when speed variations are relatively small, its performance degrades—when speed variations become relevant—when compared to the aforementioned adaptive SFOs which, in turn, still exhibit the advantage of estimating an additional critical parameter (under reliable knowledge of the motor inductance) related to demagnetization effects. Indeed, the crucial role of the adaptation is highlighted throughout the sections, while the conditions underlying the design and the stability proofs of such adaptive SFOs are shown to provide actually effective tools and restrictions under which satisfactory performances for the considered adaptive SFOs can be achieved in practice. Second, the common notation of the brief finally leads to the original formulation of a new comprehensive set of equations that simultaneously covers all the tested solutions and defines a hybrid strategy that might be very effective in practical applications.
{"title":"Stator Flux Observers for Speed-Controlled PMSMs in Low-Speed Sensorless Applications: Comparative Tests and Hybrid Strategy","authors":"Emilio Carfagna;Cristiano Maria Verrelli;Giovanni Migliazza;Fabio Bernardi;Emilio Lorenzani","doi":"10.1109/TCST.2025.3536203","DOIUrl":"https://doi.org/10.1109/TCST.2025.3536203","url":null,"abstract":"In-depth performance analysis of stator flux observers (SFOs) is carried out, in the very low-speed range, for sensorless speed-controlled drives based on permanent magnet synchronous machines (PMSMs), in the presence of no idealities of the voltage source inverter (VSI) and uncertainties in the motor electrical parameters. The original contribution of this brief is twofold. First, it relies on the presentation of experiments, within this framework, which comparatively illustrate the closed-loop performance of: 1) a stator flux (open-loop) estimator with a low-pass filter (LPF), endowed with an additional phase shift and magnitude compensation based on the estimation of machine speed and 2) two adaptive observers constituting the most recent representatives of the class of the theoretically based contributions for PMSMs. While the former can achieve more satisfactory results when speed variations are relatively small, its performance degrades—when speed variations become relevant—when compared to the aforementioned adaptive SFOs which, in turn, still exhibit the advantage of estimating an additional critical parameter (under reliable knowledge of the motor inductance) related to demagnetization effects. Indeed, the crucial role of the adaptation is highlighted throughout the sections, while the conditions underlying the design and the stability proofs of such adaptive SFOs are shown to provide actually effective tools and restrictions under which satisfactory performances for the considered adaptive SFOs can be achieved in practice. Second, the common notation of the brief finally leads to the original formulation of a new comprehensive set of equations that simultaneously covers all the tested solutions and defines a hybrid strategy that might be very effective in practical applications.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 5","pages":"1905-1912"},"PeriodicalIF":3.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891196","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 : 2025-02-04DOI: 10.1109/TCST.2025.3533312
Anthony Siming Chen;Guido Herrmann;Reza Islam;Chris Brace;James W. G. Turner;Stuart Burgess
We propose a new Q-learning-based air-fuel ratio (AFR) controller for a Wankel rotary engine. We first present a mean-value engine model (MVEM) that is modified based on the rotary engine dynamics. The AFR regulation problem is reformulated as an optimal proportional-integral (PI) controller for fuel tracking over the augmented error dynamics. Leveraging the generalized-Hamilton-Jacobi–Bellman (GHJB) equation, we propose a new definition of the Q-function with its arguments being the augmented error and the injected fuel flow rate. We then derive its Q-learning Bellman (QLB) equation based on the optimality principle. This allows online learning of a controller via an adaptive critic network for solving the QLB equation, of which the solution satisfies the GHJB equation. The proposed model-free Q-learning-based controller is implemented on an AIE 225CS Wankel engine, where the practical experiments validate the optimality and performance of the proposed controller.
{"title":"Q-Learning-Based Optimal Control via Adaptive Critic Network for a Wankel Rotary Engine","authors":"Anthony Siming Chen;Guido Herrmann;Reza Islam;Chris Brace;James W. G. Turner;Stuart Burgess","doi":"10.1109/TCST.2025.3533312","DOIUrl":"https://doi.org/10.1109/TCST.2025.3533312","url":null,"abstract":"We propose a new Q-learning-based air-fuel ratio (AFR) controller for a Wankel rotary engine. We first present a mean-value engine model (MVEM) that is modified based on the rotary engine dynamics. The AFR regulation problem is reformulated as an optimal proportional-integral (PI) controller for fuel tracking over the augmented error dynamics. Leveraging the generalized-Hamilton-Jacobi–Bellman (GHJB) equation, we propose a new definition of the Q-function with its arguments being the augmented error and the injected fuel flow rate. We then derive its Q-learning Bellman (QLB) equation based on the optimality principle. This allows online learning of a controller via an adaptive critic network for solving the QLB equation, of which the solution satisfies the GHJB equation. The proposed model-free Q-learning-based controller is implemented on an AIE 225CS Wankel engine, where the practical experiments validate the optimality and performance of the proposed controller.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"1101-1109"},"PeriodicalIF":4.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883415","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}
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