Pub Date : 2026-03-01Epub Date: 2026-01-30DOI: 10.1016/j.ifacsc.2026.100372
Yuki Miyoshi , Masaki Inoue , Yusuke Fujimoto
Natural language data, such as text and speech, have become readily available through social networking services and chat platforms. By leveraging human observations expressed in natural language, this paper addresses the problem of state estimation for physical systems, in which humans act as sensing agents. To this end, we propose a Language-Aided Particle Filter (LAPF), a particle filter framework that structures human observations via natural language processing and incorporates them into the update step of the state estimation. Finally, the LAPF is applied to the water level estimation problem in an irrigation canal and its effectiveness is demonstrated.
{"title":"Language-aided state estimation","authors":"Yuki Miyoshi , Masaki Inoue , Yusuke Fujimoto","doi":"10.1016/j.ifacsc.2026.100372","DOIUrl":"10.1016/j.ifacsc.2026.100372","url":null,"abstract":"<div><div>Natural language data, such as text and speech, have become readily available through social networking services and chat platforms. By leveraging human observations expressed in natural language, this paper addresses the problem of state estimation for physical systems, in which humans act as sensing agents. To this end, we propose a Language-Aided Particle Filter (LAPF), a particle filter framework that structures human observations via natural language processing and incorporates them into the update step of the state estimation. Finally, the LAPF is applied to the water level estimation problem in an irrigation canal and its effectiveness is demonstrated.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100372"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-03DOI: 10.1016/j.ifacsc.2026.100377
Yunyan Lee , Julian Berberich , Ian R. Petersen , Daoyi Dong
We present a data-driven framework for controlling a single qubit based on experimental data, without requiring explicit Hamiltonian models. Two modeling approaches are studied. The indirect approach identifies an affine model of the qubit dynamics and employs it for control design, while the direct approach uses a Hankel matrix representation to generate feasible control actions directly from recorded trajectories. We provide stability guarantees and verify both formulations in simulation, demonstrating that data-driven predictive control can effectively steer a qubit to the desired target state under input constraints.
{"title":"Data-driven control of a single-qubit system based on unitary evolution reconstruction","authors":"Yunyan Lee , Julian Berberich , Ian R. Petersen , Daoyi Dong","doi":"10.1016/j.ifacsc.2026.100377","DOIUrl":"10.1016/j.ifacsc.2026.100377","url":null,"abstract":"<div><div>We present a data-driven framework for controlling a single qubit based on experimental data, without requiring explicit Hamiltonian models. Two modeling approaches are studied. The indirect approach identifies an affine model of the qubit dynamics and employs it for control design, while the direct approach uses a Hankel matrix representation to generate feasible control actions directly from recorded trajectories. We provide stability guarantees and verify both formulations in simulation, demonstrating that data-driven predictive control can effectively steer a qubit to the desired target state under input constraints.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100377"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-17DOI: 10.1016/j.ifacsc.2026.100385
Jindřich Duník , Jakub Matoušek , Jan Krejčí , Marek Brandner , Yeongkwon Choe
This paper deals with the state estimation of non-linear and non-Gaussian systems with an emphasis on the numerical solution to the Bayesian recursive relations. In particular, this paper builds upon the Lagrangian grid-based filter (GbF) recently-developed for linear systems and extends it for systems with nonlinear dynamics that are invertible. The proposed nonlinear Lagrangian GbF reduces the computational complexity of the standard GbFs from quadratic to log-linear, while preserving all the strengths of the original GbF such as robustness, accuracy, and deterministic behaviour. The proposed filter is compared with the particle filter in several numerical studies using the publicly available MATLAB® implementation.
{"title":"Lagrangian grid-based estimation of nonlinear systems with invertible dynamics","authors":"Jindřich Duník , Jakub Matoušek , Jan Krejčí , Marek Brandner , Yeongkwon Choe","doi":"10.1016/j.ifacsc.2026.100385","DOIUrl":"10.1016/j.ifacsc.2026.100385","url":null,"abstract":"<div><div>This paper deals with the state estimation of non-linear and non-Gaussian systems with an emphasis on the numerical solution to the Bayesian recursive relations. In particular, this paper builds upon the Lagrangian grid-based filter (GbF) recently-developed for linear systems and extends it for systems with nonlinear dynamics that are invertible. The proposed nonlinear Lagrangian GbF reduces the computational complexity of the standard GbFs from quadratic to log-linear, while preserving all the strengths of the original GbF such as robustness, accuracy, and deterministic behaviour. The proposed filter is compared with the particle filter in several numerical studies using the publicly available MATLAB® implementation.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100385"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-23DOI: 10.1016/j.ifacsc.2026.100369
Yusuke Fujimoto , Yuki Minami
This paper discusses the data-driven design of a dynamic quantizer for control systems with discrete-valued input. We consider a quantizer with a noise-shaping filter that converts the continuous-valued input into the discrete-valued input, and discuss how to optimize the filter to minimize the error between the system outputs with and without quantization. It is known that this output deterioration can be measured by the norm of a transfer function that depends on both the system and the noise-shaping filter. This paper focuses on data-driven estimation of the norm from its input–output data, and virtually constructs input–output data for the transfer function. Then the output deterioration is minimized by minimizing this norm. The effectiveness of the proposed approach is demonstrated through a numerical example.
{"title":"Data-driven design of dynamic quantizers applicable to nonminimum phase systems","authors":"Yusuke Fujimoto , Yuki Minami","doi":"10.1016/j.ifacsc.2026.100369","DOIUrl":"10.1016/j.ifacsc.2026.100369","url":null,"abstract":"<div><div>This paper discusses the data-driven design of a dynamic quantizer for control systems with discrete-valued input. We consider a quantizer with a noise-shaping filter that converts the continuous-valued input into the discrete-valued input, and discuss how to optimize the filter to minimize the error between the system outputs with and without quantization. It is known that this output deterioration can be measured by the <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> norm of a transfer function that depends on both the system and the noise-shaping filter. This paper focuses on data-driven estimation of the <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> norm from its input–output data, and virtually constructs input–output data for the transfer function. Then the output deterioration is minimized by minimizing this <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> norm. The effectiveness of the proposed approach is demonstrated through a numerical example.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100369"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-04DOI: 10.1016/j.ifacsc.2026.100382
Jaffar Ali Lone , Rohit Kumar Singh , Shovan Bhaumik
Accurate estimation of battery state of charge (SOC) and model parameters is essential for ensuring the safety, reliability, and efficiency of modern battery management systems. Conventional filtering methods such as the extended Kalman filter rely on local approximations that can degrade in accuracy when nonlinearities are significant. This paper proposes a Gaussian integral-based filtering framework for joint SOC and parameter estimation of lithium-ion batteries modeled with an equivalent circuit representation. The key advantage lies in exploiting the polynomial structure of the SOC–OCV (open-circuit voltage) relation, which enables exact evaluation of Gaussian integrals for mean and covariance propagation. The model is validated against experimental data, and performance is assessed under an urban dynamometer driving schedule. The obtained results demonstrate that the proposed filter achieves more accurate SOC estimation and parameter tracking than the other conventional Kalman filter variants, confirming its effectiveness as a practical solution for real-time battery management under dynamic operating conditions.
{"title":"Joint battery state of charge and parameter estimation using Gaussian integral based Kalman filtering","authors":"Jaffar Ali Lone , Rohit Kumar Singh , Shovan Bhaumik","doi":"10.1016/j.ifacsc.2026.100382","DOIUrl":"10.1016/j.ifacsc.2026.100382","url":null,"abstract":"<div><div>Accurate estimation of battery state of charge (SOC) and model parameters is essential for ensuring the safety, reliability, and efficiency of modern battery management systems. Conventional filtering methods such as the extended Kalman filter rely on local approximations that can degrade in accuracy when nonlinearities are significant. This paper proposes a Gaussian integral-based filtering framework for joint SOC and parameter estimation of lithium-ion batteries modeled with an equivalent circuit representation. The key advantage lies in exploiting the polynomial structure of the SOC–OCV (open-circuit voltage) relation, which enables exact evaluation of Gaussian integrals for mean and covariance propagation. The model is validated against experimental data, and performance is assessed under an urban dynamometer driving schedule. The obtained results demonstrate that the proposed filter achieves more accurate SOC estimation and parameter tracking than the other conventional Kalman filter variants, confirming its effectiveness as a practical solution for real-time battery management under dynamic operating conditions.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100382"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-03DOI: 10.1016/j.ifacsc.2026.100373
Abdelkader Metakalard , Fabien Lauer , Kevin Colin , Marion Gilson
This paper provides statistical guarantees on the accuracy of dynamical models learned from dependent data sequences. Specifically, we develop uniform error bounds that apply to quantized models and imperfect optimization algorithms commonly used in practical contexts for system identification, and in particular hybrid system identification. Two families of bounds are obtained: slow-rate bounds via a block decomposition and fast-rate, variance-adaptive, bounds via a novel spaced-point strategy. The bounds scale with the number of bits required to encode the model and thus translate hardware constraints into interpretable statistical complexities.
{"title":"Uniform error bounds for quantized dynamical models","authors":"Abdelkader Metakalard , Fabien Lauer , Kevin Colin , Marion Gilson","doi":"10.1016/j.ifacsc.2026.100373","DOIUrl":"10.1016/j.ifacsc.2026.100373","url":null,"abstract":"<div><div>This paper provides statistical guarantees on the accuracy of dynamical models learned from dependent data sequences. Specifically, we develop uniform error bounds that apply to quantized models and imperfect optimization algorithms commonly used in practical contexts for system identification, and in particular hybrid system identification. Two families of bounds are obtained: slow-rate bounds via a block decomposition and fast-rate, variance-adaptive, bounds via a novel spaced-point strategy. The bounds scale with the number of bits required to encode the model and thus translate hardware constraints into interpretable statistical complexities.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100373"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes a dual-rate, data-driven system for automated ergometer load adjustment using Heart Rate (HR) and Heart Rate Variability (HRV). The system continuously collects HR and HRV data during exercise to estimate the user’s real-time physiological state and dynamically adjust resistance, maintaining exercise intensity tailored to individual responses. Validation with human participants demonstrated improved HRV without compromising HR tracking performance, highlighting the potential of this approach for personalized training in clinical rehabilitation, athlete conditioning, and general fitness.
{"title":"Motion data-driven exercise design for the simultaneous enhancement of physical capability and psychological state","authors":"Takao Sato, Yoshiharu Kawahara, Natsuki Kawaguchi, Yusuke Tsunoda","doi":"10.1016/j.ifacsc.2025.100349","DOIUrl":"10.1016/j.ifacsc.2025.100349","url":null,"abstract":"<div><div>This study proposes a dual-rate, data-driven system for automated ergometer load adjustment using Heart Rate (HR) and Heart Rate Variability (HRV). The system continuously collects HR and HRV data during exercise to estimate the user’s real-time physiological state and dynamically adjust resistance, maintaining exercise intensity tailored to individual responses. Validation with human participants demonstrated improved HRV without compromising HR tracking performance, highlighting the potential of this approach for personalized training in clinical rehabilitation, athlete conditioning, and general fitness.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100349"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-16DOI: 10.1016/j.ifacsc.2025.100353
Sohaib Ahmad Sirwal , Babar Ahmad , Majid Hameed Koul
Haptic feedback is essential for intuitive teleoperation, yet designing systems that improve performance without increasing cognitive load remains a critical challenge. This study investigates how the quality of vibrotactile feedback within a multimodal framework influences operator performance and control strategy. A vision-assisted haptic teleoperation system that combines position-error-based force feedback with vibrotactile cues derived from real-time contour detection is proposed. Using a low-cost dual Novint Falcon setup, a user study compared binary vibration with a graded mode employing PWM-based signals to encode proximity. The results demonstrated that graded feedback allowed participants to complete tasks 17% faster with approximately 5% lower RMSE while applying a comparable force. Subjective evaluations also revealed a 32% reduction in mental demand and a 35% reduction in frustration at NASA-TLX, in addition to significantly greater confidence and perceived performance. These findings show that proportional anticipatory feedback allows operators to shift from reactive error correction to more fluid and efficient predictive control strategies. The results infer that the quality and intuitiveness of haptic information is decisive in developing effective telepresence systems, with graded multimodal cues providing clear advantages over binary feedback in the surgical, industrial, and assistive domains.
{"title":"Multimodal haptic feedback guidance and discrimination in vision-assisted teleoperation","authors":"Sohaib Ahmad Sirwal , Babar Ahmad , Majid Hameed Koul","doi":"10.1016/j.ifacsc.2025.100353","DOIUrl":"10.1016/j.ifacsc.2025.100353","url":null,"abstract":"<div><div>Haptic feedback is essential for intuitive teleoperation, yet designing systems that improve performance without increasing cognitive load remains a critical challenge. This study investigates how the quality of vibrotactile feedback within a multimodal framework influences operator performance and control strategy. A vision-assisted haptic teleoperation system that combines position-error-based force feedback with vibrotactile cues derived from real-time contour detection is proposed. Using a low-cost dual Novint Falcon setup, a user study compared binary vibration with a graded mode employing PWM-based signals to encode proximity. The results demonstrated that graded feedback allowed participants to complete tasks 17% faster with approximately 5% lower RMSE while applying a comparable force. Subjective evaluations also revealed a 32% reduction in mental demand and a 35% reduction in frustration at NASA-TLX, in addition to significantly greater confidence and perceived performance. These findings show that proportional anticipatory feedback allows operators to shift from reactive error correction to more fluid and efficient predictive control strategies. The results infer that the quality and intuitiveness of haptic information is decisive in developing effective telepresence systems, with graded multimodal cues providing clear advantages over binary feedback in the surgical, industrial, and assistive domains.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100353"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-08DOI: 10.1016/j.ifacsc.2025.100360
Gioele Buriani , Jingyue Liu , Maximilian Stölzle , Cosimo Della Santina , Jiatao Ding
Reduced-order models are central to motion planning and control of quadruped robots, yet existing templates are often hand-crafted for a specific locomotion modality. This motivates the need for automatic methods that extract task-specific, interpretable low-dimensional dynamics directly from data. We propose a methodology that combines a linear autoencoder with symbolic regression to derive such models. The linear autoencoder provides a consistent latent embedding for configurations, velocities, accelerations, and inputs, enabling the sparse identification of nonlinear dynamics (SINDy) to operate in a compact, physics-aligned space. A multi-phase, hybrid-aware training scheme ensures coherent latent coordinates across contact transitions. We focus our validation on quadruped jumping—a representative, challenging, yet contained scenario in which a principled template model is especially valuable. The resulting symbolic dynamics outperform the state-of-the-art handcrafted actuated spring-loaded inverted pendulum (aSLIP) baseline in simulation and hardware across multiple robots and jumping modalities.
{"title":"Symbolic learning of interpretable reduced-order models for jumping quadruped robots","authors":"Gioele Buriani , Jingyue Liu , Maximilian Stölzle , Cosimo Della Santina , Jiatao Ding","doi":"10.1016/j.ifacsc.2025.100360","DOIUrl":"10.1016/j.ifacsc.2025.100360","url":null,"abstract":"<div><div>Reduced-order models are central to motion planning and control of quadruped robots, yet existing templates are often hand-crafted for a specific locomotion modality. This motivates the need for automatic methods that extract task-specific, interpretable low-dimensional dynamics directly from data. We propose a methodology that combines a linear autoencoder with symbolic regression to derive such models. The linear autoencoder provides a consistent latent embedding for configurations, velocities, accelerations, and inputs, enabling the sparse identification of nonlinear dynamics (SINDy) to operate in a compact, physics-aligned space. A multi-phase, hybrid-aware training scheme ensures coherent latent coordinates across contact transitions. We focus our validation on quadruped jumping—a representative, challenging, yet contained scenario in which a principled template model is especially valuable. The resulting symbolic dynamics outperform the state-of-the-art handcrafted actuated spring-loaded inverted pendulum (aSLIP) baseline in simulation and hardware across multiple robots and jumping modalities.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100360"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-14DOI: 10.1016/j.ifacsc.2026.100363
Eram Taslima, Shyam Kamal, R.K. Saket
This paper addresses the challenge of state estimation for two-level quantum systems governed by stochastic master equations, particularly when key Hamiltonian parameters are unknown. The critical parameters such as the qubit resonance frequency and the decay rate play a crucial role in determining system dynamics, hence their accurate estimation is essential for reliable state reconstruction. A robust framework based on the cubature Kalman filter (CKF) is developed that effectively handles both correlated and decorrelated noise processes inherent to quantum homodyne measurement. The proposed approach effectively mitigates performance degradation caused by parametric uncertainty, providing enhanced adaptability and robustness. Numerical simulations on a qubit in a cavity show that the CKF-based method achieves better estimation accuracy and faster convergence compared to the extended Kalman filter.
{"title":"Joint state and parameter estimation in quantum systems using cubature Kalman filtering","authors":"Eram Taslima, Shyam Kamal, R.K. Saket","doi":"10.1016/j.ifacsc.2026.100363","DOIUrl":"10.1016/j.ifacsc.2026.100363","url":null,"abstract":"<div><div>This paper addresses the challenge of state estimation for two-level quantum systems governed by stochastic master equations, particularly when key Hamiltonian parameters are unknown. The critical parameters such as the qubit resonance frequency and the decay rate play a crucial role in determining system dynamics, hence their accurate estimation is essential for reliable state reconstruction. A robust framework based on the cubature Kalman filter (CKF) is developed that effectively handles both correlated and decorrelated noise processes inherent to quantum homodyne measurement. The proposed approach effectively mitigates performance degradation caused by parametric uncertainty, providing enhanced adaptability and robustness. Numerical simulations on a qubit in a cavity show that the CKF-based method achieves better estimation accuracy and faster convergence compared to the extended Kalman filter.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100363"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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