Pub 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":"2026-01-30","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}
The model reference adaptive control system is an adaptive controller that maintains the control performance even when the uncertainty of the controlled system’s parameters is high, and its design methodology is well established. In particular, the direct MRACS excels in responsiveness; however, it suffers from the problem that its adjustable parameters do not converge to their true values. To converge the adjustable parameters to their true values, a conventional method involves injecting a probing signal to satisfy the PE property; however, this compromises the control performance. Thus, a control error-based probing signal auto-elimination scheme is proposed in this study, which adaptively regulates the probing signal based solely on the control error without predefined elimination timing. This enables the identification of adjustable parameters during transient phases, while automatically suppressing the probing signal once sufficient tracking performance is achieved. Furthermore, unlike existing probing-based methods, the proposed scheme allows re-injection of the probing signal when performance degradation is detected, thereby achieving a compatible realisation of identification and control within a single framework. Therefore, the proposed scheme simultaneously contributes to the identification and control, significantly reducing the tracking error. The validity of the proposed structure was confirmed by simulations under plant variation conditions.
{"title":"Compatible realisation of control and identification of direct adaptive control via probing signal auto-elimination","authors":"Akira Takakura , Takashi Yokoyama , Takahiro Nozaki , Shuichi Adachi , Hiromitsu Ohmori","doi":"10.1016/j.ifacsc.2026.100375","DOIUrl":"10.1016/j.ifacsc.2026.100375","url":null,"abstract":"<div><div>The model reference adaptive control system is an adaptive controller that maintains the control performance even when the uncertainty of the controlled system’s parameters is high, and its design methodology is well established. In particular, the direct MRACS excels in responsiveness; however, it suffers from the problem that its adjustable parameters do not converge to their true values. To converge the adjustable parameters to their true values, a conventional method involves injecting a probing signal to satisfy the PE property; however, this compromises the control performance. Thus, a control error-based probing signal auto-elimination scheme is proposed in this study, which adaptively regulates the probing signal based solely on the control error without predefined elimination timing. This enables the identification of adjustable parameters during transient phases, while automatically suppressing the probing signal once sufficient tracking performance is achieved. Furthermore, unlike existing probing-based methods, the proposed scheme allows re-injection of the probing signal when performance degradation is detected, thereby achieving a compatible realisation of identification and control within a single framework. Therefore, the proposed scheme simultaneously contributes to the identification and control, significantly reducing the tracking error. The validity of the proposed structure was confirmed by simulations under plant variation conditions.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100375"},"PeriodicalIF":1.8,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077720","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 : 2026-01-29DOI: 10.1016/j.ifacsc.2026.100371
Sarasij Banerjee , Eric Hekler , Daniel E. Rivera
This paper presents a methodology for optimizing “plant-friendly” multisine input signals to identify nonlinear dynamic systems under time-domain input and output constraints, without requiring a global parametric model a priori. The goal is to construct an informative dataset for open-loop, data-driven identification while selecting operational requirements. A weighted optimization framework is proposed to minimize the output crest factor resulting from a data-driven model, with penalties for violating input and output constraints. Model-on-Demand (MoD) estimation is employed to simulate outputs using prior data, effectively predicting nonlinear responses without global modeling. This MoD-based formulation enables evaluating output crest factors and output constraint compliance with modest modeling effort and improved impact. The resulting non-smooth, non-convex problem is solved using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, which perturbs the multisine phase vector to achieve the desired performance efficiently. This method supports the concept of identification test monitoring, as illustrated in this paper. Within the identification test loops, each optimized excitation is applied to gather new estimation data, iteratively refining MoD-based output predictions and improving constraint satisfaction. The method’s effectiveness is demonstrated through a safety-critical case study on a Susceptible-Infected-Recovered (SIR) epidemiological network, showing that the optimized excitation yields highly informative data for identification while keeping the infection spread within safe limits.
{"title":"Multisine input signal design for constrained, “plant-friendly” system identification of nonlinear systems","authors":"Sarasij Banerjee , Eric Hekler , Daniel E. Rivera","doi":"10.1016/j.ifacsc.2026.100371","DOIUrl":"10.1016/j.ifacsc.2026.100371","url":null,"abstract":"<div><div>This paper presents a methodology for optimizing “plant-friendly” multisine input signals to identify nonlinear dynamic systems under time-domain input and output constraints, without requiring a global parametric model <em>a priori</em>. The goal is to construct an informative dataset for open-loop, data-driven identification while selecting operational requirements. A weighted optimization framework is proposed to minimize the output crest factor resulting from a data-driven model, with penalties for violating input and output constraints. Model-on-Demand (MoD) estimation is employed to simulate outputs using prior data, effectively predicting nonlinear responses without global modeling. This MoD-based formulation enables evaluating output crest factors and output constraint compliance with modest modeling effort and improved impact. The resulting non-smooth, non-convex problem is solved using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, which perturbs the multisine phase vector to achieve the desired performance efficiently. This method supports the concept of <em>identification test monitoring</em>, as illustrated in this paper. Within the identification test loops, each optimized excitation is applied to gather new estimation data, iteratively refining MoD-based output predictions and improving constraint satisfaction. The method’s effectiveness is demonstrated through a safety-critical case study on a Susceptible-Infected-Recovered (SIR) epidemiological network, showing that the optimized excitation yields highly informative data for identification while keeping the infection spread within safe limits.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100371"},"PeriodicalIF":1.8,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077766","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 : 2026-01-29DOI: 10.1016/j.ifacsc.2026.100370
Hesham Abdelfattah , Sameh A. Eisa , Peter Stechlinski
In this paper, we provide a novel framework that enables a sensitivity-based observability test and state estimation algorithm for wind turbine power systems (WTPSs). The provided framework is the first of its kind in the literature, as it is able to deal with state-of-the-art WTPS models that are non-reduced, highly nonlinear differential–algebraic equation systems. Moreover, the framework includes nonsmoothness in both the dynamics and output functions to unify the operational conditions over different wind speed regions. We demonstrate the effectiveness of the proposed framework (thanks to the underlying tools from generalized derivatives theory) on different wind speed profiles, including real-world wind data. We also illustrate how the proposed framework, by the utilization of robust observability analysis during nonsmooth transitions, enables accurate state estimation for cases when the conventional Extended Kalman Filter approach fails.
{"title":"Observability analysis and state estimation of wind turbine power systems: A novel sensitivity-based approach","authors":"Hesham Abdelfattah , Sameh A. Eisa , Peter Stechlinski","doi":"10.1016/j.ifacsc.2026.100370","DOIUrl":"10.1016/j.ifacsc.2026.100370","url":null,"abstract":"<div><div>In this paper, we provide a novel framework that enables a sensitivity-based observability test and state estimation algorithm for wind turbine power systems (WTPSs). The provided framework is the first of its kind in the literature, as it is able to deal with state-of-the-art WTPS models that are non-reduced, highly nonlinear differential–algebraic equation systems. Moreover, the framework includes nonsmoothness in both the dynamics and output functions to unify the operational conditions over different wind speed regions. We demonstrate the effectiveness of the proposed framework (thanks to the underlying tools from generalized derivatives theory) on different wind speed profiles, including real-world wind data. We also illustrate how the proposed framework, by the utilization of robust observability analysis during nonsmooth transitions, enables accurate state estimation for cases when the conventional Extended Kalman Filter approach fails.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100370"},"PeriodicalIF":1.8,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173697","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 : 2026-01-23DOI: 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":"2026-01-23","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 : 2026-01-23DOI: 10.1016/j.ifacsc.2026.100367
Mohamed Arnouss , Yezekael Hayel , Karam Allali
Economic savings achieved through targeted isolation avoid additional disease burdens and effectively address the disease-economy trade-offs in epidemic control. In this study, we use phase-space analysis to derive the explicit solution of the optimal control problem that minimize the infection peak given budget limitation. The optimal policy obtained is an adaptive control where the isolation rate dynamically adjusts according to the current epidemic state. We show that targeted isolation control policy achieves the same infection peak as transmission reduction policies under equivalent budgets, while avoiding broad socio-economic disruptions. Additionally, we show through numerical simulations that the control resolves the epidemic faster and reduces total infections. This demonstrates that targeted isolation can strike a balance between public health and economic stability, offering actionable insights for public health decisions moving forward.
{"title":"Adaptive optimal resource allocation for isolation interventions: Flattening the curve","authors":"Mohamed Arnouss , Yezekael Hayel , Karam Allali","doi":"10.1016/j.ifacsc.2026.100367","DOIUrl":"10.1016/j.ifacsc.2026.100367","url":null,"abstract":"<div><div>Economic savings achieved through targeted isolation avoid additional disease burdens and effectively address the disease-economy trade-offs in epidemic control. In this study, we use phase-space analysis to derive the explicit solution of the optimal control problem that minimize the infection peak given budget limitation. The optimal policy obtained is an adaptive control where the isolation rate dynamically adjusts according to the current epidemic state. We show that targeted isolation control policy achieves the same infection peak as transmission reduction policies under equivalent budgets, while avoiding broad socio-economic disruptions. Additionally, we show through numerical simulations that the control resolves the epidemic faster and reduces total infections. This demonstrates that targeted isolation can strike a balance between public health and economic stability, offering actionable insights for public health decisions moving forward.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100367"},"PeriodicalIF":1.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173694","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}
Human-in-the-loop calibration is often addressed via preference-based optimization, where algorithms learn from pairwise comparisons rather than explicit cost evaluations. While effective, methods such as Preferential Bayesian Optimization or Global optimization based on active preference learning with radial basis functions (GLISp) treat the system as a black box and ignore informative sensor measurements. In this work, we introduce a sensor-guided regularized extension of GLISp that integrates measurable descriptors into the preference-learning loop through a physics-informed hypothesis function and a least-squares regularization term. This injects grey-box structure, combining subjective feedback with quantitative sensor information while preserving the flexibility of preference-based search. Numerical evaluations on an analytical benchmark and on a human-in-the-loop vehicle suspension tuning task show faster convergence and superior final solutions compared to baseline GLISp.
{"title":"Regularized GLISp for sensor-guided human-in-the-loop optimization","authors":"Matteo Cercola , Michele Lomuscio , Dario Piga , Simone Formentin","doi":"10.1016/j.ifacsc.2026.100368","DOIUrl":"10.1016/j.ifacsc.2026.100368","url":null,"abstract":"<div><div>Human-in-the-loop calibration is often addressed via preference-based optimization, where algorithms learn from pairwise comparisons rather than explicit cost evaluations. While effective, methods such as Preferential Bayesian Optimization or Global optimization based on active preference learning with radial basis functions (GLISp) treat the system as a black box and ignore informative sensor measurements. In this work, we introduce a sensor-guided regularized extension of GLISp that integrates measurable descriptors into the preference-learning loop through a physics-informed hypothesis function and a least-squares regularization term. This injects grey-box structure, combining subjective feedback with quantitative sensor information while preserving the flexibility of preference-based search. Numerical evaluations on an analytical benchmark and on a human-in-the-loop vehicle suspension tuning task show faster convergence and superior final solutions compared to baseline GLISp.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100368"},"PeriodicalIF":1.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022733","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 : 2026-01-21DOI: 10.1016/j.ifacsc.2026.100366
Thomas Banker, Nathan P. Lawrence, Ali Mesbah
A major challenge in reinforcement learning (RL) is guaranteeing an agent’s closed-loop stability under unknown, possibly sparse, reward functions. While model-free RL is flexible to a variety of systems and rewards, model-based control strategies such as optimization-based control naturally accommodate prior system models to provide guarantees on safety and stability. However, these models may not be representative of the true global performance objective, resulting in suboptimal policies. In this paper, we present a policy search RL approach that decouples the stability requirement from the global performance objective. The key idea is to use an optimization-based policy structure as an effective stabilizing parameterization with which the agent can learn to maximize an unknown reward in a model-free fashion. Specifically, the agent employs a predictive control architecture and implicitly learns a stabilizing terminal cost, which is constructed through fixed-point iterations of the discrete algebraic Riccati equation. By implicitly differentiating this fixed-point, derivatives of the stability condition inform policy gradients. The proposed approach is shown to design high-performance, stabilizing policies for various sparse, global performance objectives. Furthermore, the proposed approach can account for uncertainty in the dynamics using the stochastic discrete algebraic Riccati equation to promote robust stability. This work demonstrates a principled policy search RL approach, integrating prior models and system observations in an agent’s design, towards safe and reliable decision-making under uncertainty.
{"title":"Stability-constrained policy optimization under unknown rewards","authors":"Thomas Banker, Nathan P. Lawrence, Ali Mesbah","doi":"10.1016/j.ifacsc.2026.100366","DOIUrl":"10.1016/j.ifacsc.2026.100366","url":null,"abstract":"<div><div>A major challenge in reinforcement learning (RL) is guaranteeing an agent’s closed-loop stability under unknown, possibly sparse, reward functions. While model-free RL is flexible to a variety of systems and rewards, model-based control strategies such as optimization-based control naturally accommodate prior system models to provide guarantees on safety and stability. However, these models may not be representative of the true global performance objective, resulting in suboptimal policies. In this paper, we present a policy search RL approach that decouples the stability requirement from the global performance objective. The key idea is to use an optimization-based policy structure as an effective stabilizing parameterization with which the agent can learn to maximize an unknown reward in a model-free fashion. Specifically, the agent employs a predictive control architecture and implicitly learns a stabilizing terminal cost, which is constructed through fixed-point iterations of the discrete algebraic Riccati equation. By implicitly differentiating this fixed-point, derivatives of the stability condition inform policy gradients. The proposed approach is shown to design high-performance, stabilizing policies for various sparse, global performance objectives. Furthermore, the proposed approach can account for uncertainty in the dynamics using the stochastic discrete algebraic Riccati equation to promote robust stability. This work demonstrates a principled policy search RL approach, integrating prior models and system observations in an agent’s design, towards safe and reliable decision-making under uncertainty.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100366"},"PeriodicalIF":1.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077767","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 : 2026-01-15DOI: 10.1016/j.ifacsc.2026.100365
Mazen Alamir
This paper leverages recent advances in high derivatives reconstruction from noisy-time series and sparse multivariate polynomial identification in order to improve the process of parsimoniously identifying, from a small amount of data, unknown Single-Input/Single-Output nonlinear dynamics of relative degree up to 4. The methodology is illustrated on the Electronic Throttle Controlled automotive system.
{"title":"On continuous-time sparse identification of nonlinear polynomial systems","authors":"Mazen Alamir","doi":"10.1016/j.ifacsc.2026.100365","DOIUrl":"10.1016/j.ifacsc.2026.100365","url":null,"abstract":"<div><div>This paper leverages recent advances in high derivatives reconstruction from noisy-time series and sparse multivariate polynomial identification in order to improve the process of parsimoniously identifying, from a small amount of data, unknown Single-Input/Single-Output nonlinear dynamics of relative degree up to 4. The methodology is illustrated on the Electronic Throttle Controlled automotive system.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100365"},"PeriodicalIF":1.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977346","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 : 2026-01-14DOI: 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":"2026-01-14","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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