Pub Date : 2025-11-01DOI: 10.1016/j.ejcon.2025.101333
Emilio Corcione , Michael Kübler , Magnus Benke , Stephanie Mrzyglod , Jixing Zhang , Oliver Sawodny , Jörg Wrachtrup , Cristina Tarín
We present a novel approach to processing periodic signals with non-stationary fundamental frequency. These quasi-periodic signals feature a perpetually recurring underlying signal pattern and arise in various fields of science and engineering. The proposed method integrates a recursive extraction of the signal pattern with dynamic tracking of the instantaneous phase to effectively suppress measurement noise without prior knowledge of the signal characteristics or the frequency variation. The performance is showcased both in simulation and using experimental measurements of the cardiac cycle obtained by a nitrogen-vacancy diamond quantum sensor. Overall, high-fidelity signal reconstruction and convincing pattern learning is achieved, even in the presence of complex non-linear disturbances and non-Gaussian noise. Conclusively, the proposed technique constitutes a flexible and efficient solution, addressing limitations of existing methods and offering real-world applicability.
{"title":"Adaptive modelling and filtering of periodic signals with non-stationary fundamental frequency","authors":"Emilio Corcione , Michael Kübler , Magnus Benke , Stephanie Mrzyglod , Jixing Zhang , Oliver Sawodny , Jörg Wrachtrup , Cristina Tarín","doi":"10.1016/j.ejcon.2025.101333","DOIUrl":"10.1016/j.ejcon.2025.101333","url":null,"abstract":"<div><div>We present a novel approach to processing periodic signals with non-stationary fundamental frequency. These quasi-periodic signals feature a perpetually recurring underlying signal pattern and arise in various fields of science and engineering. The proposed method integrates a recursive extraction of the signal pattern with dynamic tracking of the instantaneous phase to effectively suppress measurement noise without prior knowledge of the signal characteristics or the frequency variation. The performance is showcased both in simulation and using experimental measurements of the cardiac cycle obtained by a nitrogen-vacancy diamond quantum sensor. Overall, high-fidelity signal reconstruction and convincing pattern learning is achieved, even in the presence of complex non-linear disturbances and non-Gaussian noise. Conclusively, the proposed technique constitutes a flexible and efficient solution, addressing limitations of existing methods and offering real-world applicability.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101333"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101337
Joyce Lai, Peter Seiler
Disturbance rejection in high-precision control applications can be significantly improved upon via online convex optimization (OCO). This includes classical techniques such as recursive least squares (RLS) and more recent, regret-based formulations. However, these methods can cause instabilities in the presence of model uncertainty. This paper introduces a safety filter for systems with OCO in the form of adaptive finite impulse response (FIR) filtering to ensure robust disturbance rejection. The safety filter enforces a robust stability constraint on the FIR coefficients while minimally altering the OCO command in the -norm cost. Additionally, we show that the induced -norm allows for easy online implementation of the safety filter by directly limiting the OCO command. The constraint can be tuned to trade off robustness and performance. We provide a simple example to demonstrate the safety filter.
{"title":"Safety filter for robust disturbance rejection via online optimization","authors":"Joyce Lai, Peter Seiler","doi":"10.1016/j.ejcon.2025.101337","DOIUrl":"10.1016/j.ejcon.2025.101337","url":null,"abstract":"<div><div>Disturbance rejection in high-precision control applications can be significantly improved upon via online convex optimization (OCO). This includes classical techniques such as recursive least squares (RLS) and more recent, regret-based formulations. However, these methods can cause instabilities in the presence of model uncertainty. This paper introduces a safety filter for systems with OCO in the form of adaptive finite impulse response (FIR) filtering to ensure robust disturbance rejection. The safety filter enforces a robust stability constraint on the FIR coefficients while minimally altering the OCO command in the <span><math><mi>∞</mi></math></span>-norm cost. Additionally, we show that the induced <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span>-norm allows for easy online implementation of the safety filter by directly limiting the OCO command. The constraint can be tuned to trade off robustness and performance. We provide a simple example to demonstrate the safety filter.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101337"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101338
Tarun Pati , Maral Mordad , Sze Zheng Yong
This article introduces polytopic observer designs for discrete- and continuous-time linear systems with bounded uncertainties. In particular, by noting that polytopes are equivalent to constrained zonotopes with intervals in their generator spaces, we propose two choices of generators to fix the order of the polytopic observer. Moreover, this observation enables us to leverage existing interval observers to find interval estimates in the augmented generator space before projecting them onto the original state space as polytopes/constrained zonotopes. Further, we prove that the polytopic observers are at least as good as interval observers for the same uncertain linear system in terms of the volumes of their set estimates and the error system gains. As a side contribution, we also introduce a more computationally efficient approach to obtain interval observer gains. Finally, we demonstrate and discuss the effectiveness of the proposed approach on a broad range of examples.
{"title":"Polytopic observer designs for uncertain linear systems","authors":"Tarun Pati , Maral Mordad , Sze Zheng Yong","doi":"10.1016/j.ejcon.2025.101338","DOIUrl":"10.1016/j.ejcon.2025.101338","url":null,"abstract":"<div><div>This article introduces polytopic observer designs for discrete- and continuous-time linear systems with bounded uncertainties. In particular, by noting that polytopes are equivalent to constrained zonotopes with intervals in their generator spaces, we propose two choices of generators to fix the order of the polytopic observer. Moreover, this observation enables us to leverage existing interval observers to find interval estimates in the augmented generator space before projecting them onto the original state space as polytopes/constrained zonotopes. Further, we prove that the polytopic observers are at least as good as interval observers for the same uncertain linear system in terms of the volumes of their set estimates and the error system gains. As a side contribution, we also introduce a more computationally efficient approach to obtain interval observer gains. Finally, we demonstrate and discuss the effectiveness of the proposed approach on a broad range of examples.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101338"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101361
Debojyoti Biswas , Eduardo D. Sontag , Noah J. Cowan
We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium point. To overcome this fundamental limitation, we propose a simple control scheme that includes a low-amplitude periodic forcing function akin to so-called “active sensing” in biology, together with nonlinear output feedback. Our analysis shows that this approach leads to the emergence of an exponentially stable limit cycle. These findings offer a provably stable active sensing strategy and may thus help to rationalize the active sensing movements animals make when performing certain motor behaviors.
{"title":"An exact active sensing strategy for a class of bio-inspired systems","authors":"Debojyoti Biswas , Eduardo D. Sontag , Noah J. Cowan","doi":"10.1016/j.ejcon.2025.101361","DOIUrl":"10.1016/j.ejcon.2025.101361","url":null,"abstract":"<div><div>We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium point. To overcome this fundamental limitation, we propose a simple control scheme that includes a low-amplitude periodic forcing function akin to so-called “active sensing” in biology, together with nonlinear output feedback. Our analysis shows that this approach leads to the emergence of an exponentially stable limit cycle. These findings offer a provably stable active sensing strategy and may thus help to rationalize the active sensing movements animals make when performing certain motor behaviors.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101361"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101293
Erik van den Eshof, Jorn van Kampen, Mauro Salazar
This paper presents an energy and thermal management system for electric race cars, where we tune a lift-off-throttle signal for the driver in real-time to respect energy budgets and thermal constraints. First, we compute globally optimal state trajectories in a real-time capable solving time, optimizing a 47 km horizon in 2.5 s. Next, for safe operation with a human driver, we simplify it to a maximum-power-or-coast operation in full-throttle regions (straights). Thereby, both the positions from which the vehicle should start coasting and the optimal throttle map are subject to tuning. To this end, we define the coasting sections with a threshold on the costate trajectory of the kinetic energy from the optimal solution. We devise an online implementable bisection algorithm to tune this threshold and adapt it using PI feedback. Finally, we validate the proposed approach for an electric endurance race car and compare three variants with varying implementation challenges: one re-optimizing and updating the kinetic costate trajectory online, one applying only the bisection algorithm online, and one relying exclusively on feedback control. Our results show that, under typical racing disturbances, our energy management achieves stint times ranging from less than 0.056% to 0.22% slower compared to offline optimization with a priori disturbance knowledge, paving the way for on-board implementations and testing.
{"title":"Human-in-the-loop energy and thermal management for electric racing cars through optimization-based control","authors":"Erik van den Eshof, Jorn van Kampen, Mauro Salazar","doi":"10.1016/j.ejcon.2025.101293","DOIUrl":"10.1016/j.ejcon.2025.101293","url":null,"abstract":"<div><div>This paper presents an energy and thermal management system for electric race cars, where we tune a lift-off-throttle signal for the driver in real-time to respect energy budgets and thermal constraints. First, we compute globally optimal state trajectories in a real-time capable solving time, optimizing a 47 km horizon in 2.5 s. Next, for safe operation with a human driver, we simplify it to a maximum-power-or-coast operation in full-throttle regions (straights). Thereby, both the positions from which the vehicle should start coasting and the optimal throttle map are subject to tuning. To this end, we define the coasting sections with a threshold on the costate trajectory of the kinetic energy from the optimal solution. We devise an online implementable bisection algorithm to tune this threshold and adapt it using PI feedback. Finally, we validate the proposed approach for an electric endurance race car and compare three variants with varying implementation challenges: one re-optimizing and updating the kinetic costate trajectory online, one applying only the bisection algorithm online, and one relying exclusively on feedback control. Our results show that, under typical racing disturbances, our energy management achieves stint times ranging from less than 0.056% to 0.22% slower compared to offline optimization with a priori disturbance knowledge, paving the way for on-board implementations and testing.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101293"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101308
Mattia Bianchi, Florian Dörfler
Online Feedback Optimization (OFO) is a control approach to drive a dynamical plant to an optimal steady state. By interconnecting optimization algorithms with real-time plant measurements, OFO provides all the benefits of feedback control, yet without requiring exact knowledge of plant dynamics for computing a setpoint. On the downside, existing stability guarantees for OFO require the controller to evolve on a sufficiently slower timescale than the plant, possibly affecting transient performance and responsiveness to disturbances. In this paper, we prove that, under suitable conditions, OFO ensures stability without any timescale separation. In particular, the condition we propose is independent of the time constant of the plant, hence it is scaling-invariant. Our analysis leverages a composite Lyapunov function, which is the of plant-related and controller-related components. We corroborate our theoretical results with numerical examples.
{"title":"A stability condition for online feedback optimization without timescale separation","authors":"Mattia Bianchi, Florian Dörfler","doi":"10.1016/j.ejcon.2025.101308","DOIUrl":"10.1016/j.ejcon.2025.101308","url":null,"abstract":"<div><div>Online Feedback Optimization (OFO) is a control approach to drive a dynamical plant to an optimal steady state. By interconnecting optimization algorithms with real-time plant measurements, OFO provides all the benefits of feedback control, yet without requiring exact knowledge of plant dynamics for computing a setpoint. On the downside, existing stability guarantees for OFO require the controller to evolve on a sufficiently slower timescale than the plant, possibly affecting transient performance and responsiveness to disturbances. In this paper, we prove that, under suitable conditions, OFO ensures stability without any timescale separation. In particular, the condition we propose is independent of the time constant of the plant, hence it is scaling-invariant. Our analysis leverages a composite Lyapunov function, which is the <span><math><mo>max</mo></math></span> of plant-related and controller-related components. We corroborate our theoretical results with numerical examples.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101308"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101319
Riccardo Zuliani , Efe C. Balta , John Lygeros
Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We propose a principled way to tune the cost function and the constraints of linear MPC schemes to improve the closed-loop performance and robust constraint satisfaction on uncertain nonlinear dynamics with additive noise. The tuning is performed using a novel MPC tuning algorithm based on backpropagation developed in our earlier work. Using the scenario approach, we provide probabilistic bounds on the likelihood of closed-loop constraint violation over a finite horizon. We showcase the effectiveness of the proposed method on linear and nonlinear simulation examples.
{"title":"Closed-loop performance optimization of model predictive control with robustness guarantees","authors":"Riccardo Zuliani , Efe C. Balta , John Lygeros","doi":"10.1016/j.ejcon.2025.101319","DOIUrl":"10.1016/j.ejcon.2025.101319","url":null,"abstract":"<div><div>Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We propose a principled way to tune the cost function and the constraints of linear MPC schemes to improve the closed-loop performance and robust constraint satisfaction on uncertain nonlinear dynamics with additive noise. The tuning is performed using a novel MPC tuning algorithm based on backpropagation developed in our earlier work. Using the scenario approach, we provide probabilistic bounds on the likelihood of closed-loop constraint violation over a finite horizon. We showcase the effectiveness of the proposed method on linear and nonlinear simulation examples.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101319"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101307
Felix Brändle, Frank Allgöwer
In this paper, we present a new parametrization to perform direct data-driven analysis and controller synthesis for the error-in-variables case. To achieve this, we employ the Sherman–Morrison–Woodbury formula to transform the problem into a linear fractional transformation with unknown measurement errors and disturbances as uncertainties. For bounded uncertainties, we apply robust control techniques to derive a guaranteed upper bound on the -norm of the unknown true system. To this end, a single semidefinite program needs to be solved, with complexity that is independent of the amount of data. Furthermore, we exploit the signal-to-noise ratio to provide a data-dependent condition, that characterizes whether the proposed parametrization can be employed. The modular formulation allows to extend this framework to controller synthesis with different performance criteria, input–output settings, and various system properties. Finally, we validate the proposed approach through a numerical example.
{"title":"A system parametrization for direct data-driven analysis and control with error-in-variables","authors":"Felix Brändle, Frank Allgöwer","doi":"10.1016/j.ejcon.2025.101307","DOIUrl":"10.1016/j.ejcon.2025.101307","url":null,"abstract":"<div><div>In this paper, we present a new parametrization to perform direct data-driven analysis and controller synthesis for the error-in-variables case. To achieve this, we employ the Sherman–Morrison–Woodbury formula to transform the problem into a linear fractional transformation with unknown measurement errors and disturbances as uncertainties. For bounded uncertainties, we apply robust control techniques to derive a guaranteed upper bound on the <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>-norm of the unknown true system. To this end, a single semidefinite program needs to be solved, with complexity that is independent of the amount of data. Furthermore, we exploit the signal-to-noise ratio to provide a data-dependent condition, that characterizes whether the proposed parametrization can be employed. The modular formulation allows to extend this framework to controller synthesis with different performance criteria, input–output settings, and various system properties. Finally, we validate the proposed approach through a numerical example.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101307"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101324
Beniamino Di Lorenzo , Gian Carlo Maffettone , Mario di Bernardo
In this paper, we address the large-scale shepherding control problem using a density control strategy based on continuification. We consider a scenario in which a large group of follower agents (targets) must be confined within a designated goal region through indirect interactions with a controllable set of leader agents (herders). Our approach transforms the microscopic agent-based dynamics into a macroscopic continuum model. This formulation enables efficient, scalable control design for the herders’ behavior, with guarantees of global convergence. The approach is validated through comprehensive numerical simulations and novel mixed-reality experiments integrating physical and virtual agents, demonstrating both its effectiveness and practical applicability.
{"title":"A continuification-based control solution for large-scale shepherding","authors":"Beniamino Di Lorenzo , Gian Carlo Maffettone , Mario di Bernardo","doi":"10.1016/j.ejcon.2025.101324","DOIUrl":"10.1016/j.ejcon.2025.101324","url":null,"abstract":"<div><div>In this paper, we address the large-scale shepherding control problem using a density control strategy based on continuification. We consider a scenario in which a large group of follower agents (targets) must be confined within a designated goal region through indirect interactions with a controllable set of leader agents (herders). Our approach transforms the microscopic agent-based dynamics into a macroscopic continuum model. This formulation enables efficient, scalable control design for the herders’ behavior, with guarantees of global convergence. The approach is validated through comprehensive numerical simulations and novel mixed-reality experiments integrating physical and virtual agents, demonstrating both its effectiveness and practical applicability.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101324"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-11-01DOI: 10.1016/j.ejcon.2025.101334
Beatrice M. Bălăceanu , Florin Stoican , Bogdan D. Ciubotaru , Ionela Prodan
The aim of this paper is to provide a Derivative-free Kalman Filter (DKF) for a six-wheeled rover, as an alternative to the classical Kalman Filters. The DKF removes the risks which the Extended Kalman Filter (EKF), based on the computation of Jacobians, pose when implemented on platforms with limited numerical precision. It is also more computationally efficient than the Unscented Kalman filters, while retaining similar performance. The DKF is designed for an Ackermann steered six-wheeled rover which has nonlinear, coupled longitudinal and lateral dynamics. Starting from the first principles of the two axle car, we derive the rover’s mathematical model, which we then express through a flat-output representation. Using the resulting Brunovsky form, we provide both a position estimate (via the DKF) and a control module (a reference tracking variation). The model and its ancillary blocks are tested in simulation to validate the flat representation’s consistency and the performance of the navigation and control actions.
{"title":"Derivative-free Kalman filtering for a six-wheeled rover","authors":"Beatrice M. Bălăceanu , Florin Stoican , Bogdan D. Ciubotaru , Ionela Prodan","doi":"10.1016/j.ejcon.2025.101334","DOIUrl":"10.1016/j.ejcon.2025.101334","url":null,"abstract":"<div><div>The aim of this paper is to provide a Derivative-free Kalman Filter (DKF) for a six-wheeled rover, as an alternative to the classical Kalman Filters. The DKF removes the risks which the Extended Kalman Filter (EKF), based on the computation of Jacobians, pose when implemented on platforms with limited numerical precision. It is also more computationally efficient than the Unscented Kalman filters, while retaining similar performance. The DKF is designed for an Ackermann steered six-wheeled rover which has nonlinear, coupled longitudinal and lateral dynamics. Starting from the first principles of the two axle car, we derive the rover’s mathematical model, which we then express through a flat-output representation. Using the resulting Brunovsky form, we provide both a position estimate (via the DKF) and a control module (a reference tracking variation). The model and its ancillary blocks are tested in simulation to validate the flat representation’s consistency and the performance of the navigation and control actions.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"86 ","pages":"Article 101334"},"PeriodicalIF":2.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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