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 : 2026-01-08DOI: 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":"2026-01-08","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 : 2026-01-08DOI: 10.1016/j.ifacsc.2026.100362
Junhua Zheng , Zhiqiang Ge , Li Sun
While deep learning has made significant achievements in the past years, it suffers from several serious shortcomings. Particularly, the performance of deep learning may be severely degraded under a small size of labeled training dataset, the case of which is quite common in industrial application scenarios although we are in the age of big data. In this paper, a semi-supervised deep model is proposed for predictive learning and data analytics, which is based upon the recently developed lightweight deep partial least squares model (PLS) structure. Precisely, the simple self-training strategy is used as the driving force to formulate the semi-supervised deep PLS model, which has no restriction in model structure and thus is flexible for predictive learning. In addition, to reduce the uncertainty of the self-training process, i.e. prediction error accumulation, different random seeds are introduced for model training, the results of which are combined together through an ensemble learning strategy. As a result, the predictive model becomes more stable and robust to those uncertainties introduced by both unlabeled data and the semi-supervised learning process. A real industrial example is provided for performance evaluation of the proposed method.
{"title":"Ensemble self-training deep partial least squares models for stable semi-supervised predictive learning and data analytics","authors":"Junhua Zheng , Zhiqiang Ge , Li Sun","doi":"10.1016/j.ifacsc.2026.100362","DOIUrl":"10.1016/j.ifacsc.2026.100362","url":null,"abstract":"<div><div>While deep learning has made significant achievements in the past years, it suffers from several serious shortcomings. Particularly, the performance of deep learning may be severely degraded under a small size of labeled training dataset, the case of which is quite common in industrial application scenarios although we are in the age of big data. In this paper, a semi-supervised deep model is proposed for predictive learning and data analytics, which is based upon the recently developed lightweight deep partial least squares model (PLS) structure. Precisely, the simple self-training strategy is used as the driving force to formulate the semi-supervised deep PLS model, which has no restriction in model structure and thus is flexible for predictive learning. In addition, to reduce the uncertainty of the self-training process, i.e. prediction error accumulation, different random seeds are introduced for model training, the results of which are combined together through an ensemble learning strategy. As a result, the predictive model becomes more stable and robust to those uncertainties introduced by both unlabeled data and the semi-supervised learning process. A real industrial example is provided for performance evaluation of the proposed method.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100362"},"PeriodicalIF":1.8,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977242","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 paper focuses on the analysis of the Region of Attraction (RoA) for unknown autonomous dynamical systems. A data-driven approach based on the moment-Sum of Squares (SoS) hierarchy is proposed, enabling novel RoA outer approximations despite the reduced information on the dynamics. The main contribution consists of bypassing the system model and, hence, the recurring constraint on its polynomial structure. Numerical experiments showcase the influence of data on learned approximating sets, highlighting the potential of this method.
{"title":"Convex computation of regions of attraction from data using sums-of-squares programming","authors":"Oumayma Khattabi , Matteo Tacchi-Bénard , Sorin Olaru","doi":"10.1016/j.ifacsc.2026.100361","DOIUrl":"10.1016/j.ifacsc.2026.100361","url":null,"abstract":"<div><div>This paper focuses on the analysis of the Region of Attraction (RoA) for unknown autonomous dynamical systems. A data-driven approach based on the moment-Sum of Squares (SoS) hierarchy is proposed, enabling novel RoA outer approximations despite the reduced information on the dynamics. The main contribution consists of bypassing the system model and, hence, the recurring constraint on its polynomial structure. Numerical experiments showcase the influence of data on learned approximating sets, highlighting the potential of this method.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100361"},"PeriodicalIF":1.8,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925982","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 : 2026-01-03DOI: 10.1016/j.ifacsc.2025.100359
Mordecai Opoku Ohemeng , Bernard Asamoah Afful , Joseph Ackora-Prah , Benedict Barnes , Ishmael Takyi
This paper develops a general Lyapunov-based framework for state-feedback control of nonlinear discrete-time systems, where the controller is designed to offer formal assurance of the monotonic decrease of a quadratic Lyapunov function while explicitly accounting for actuator saturation (input constraints). The framework is first presented in a general setting, emphasizing stability conditions under practical limits, and then applied to a case study in autonomous driving. The core difficulty lies in analytically deriving the feedback gains to satisfy both the Hurwitz stability criteria and desired transient (damping) specifications, while maintaining a low-complexity structure. Using a simplified vehicle model, two controllers are compared: a basic proportional feedback law and a Lyapunov-stable controller (LSC) that explicitly incorporates lateral deviation into the control policy. Both controllers are evaluated on real-world driving trajectories from the comma2k19 dataset. Simulation results demonstrate that the LSC significantly improves lane-keeping performance and accelerates convergence to the equilibrium compared to the baseline controller. The novelty of this work lies in bridging Lyapunov stability analysis with practical control evaluation on real driving data, offering a systematic approach to controller design.
{"title":"Enhancing autonomous vehicle control with lateral error feedback analysis","authors":"Mordecai Opoku Ohemeng , Bernard Asamoah Afful , Joseph Ackora-Prah , Benedict Barnes , Ishmael Takyi","doi":"10.1016/j.ifacsc.2025.100359","DOIUrl":"10.1016/j.ifacsc.2025.100359","url":null,"abstract":"<div><div>This paper develops a general Lyapunov-based framework for state-feedback control of nonlinear discrete-time systems, where the controller is designed to offer formal assurance of the monotonic decrease of a quadratic Lyapunov function while explicitly accounting for actuator saturation (input constraints). The framework is first presented in a general setting, emphasizing stability conditions under practical limits, and then applied to a case study in autonomous driving. The core difficulty lies in analytically deriving the feedback gains to satisfy both the Hurwitz stability criteria and desired transient (damping) specifications, while maintaining a low-complexity structure. Using a simplified vehicle model, two controllers are compared: a basic proportional feedback law and a Lyapunov-stable controller (LSC) that explicitly incorporates lateral deviation into the control policy. Both controllers are evaluated on real-world driving trajectories from the comma2k19 dataset. Simulation results demonstrate that the LSC significantly improves lane-keeping performance and accelerates convergence to the equilibrium compared to the baseline controller. The novelty of this work lies in bridging Lyapunov stability analysis with practical control evaluation on real driving data, offering a systematic approach to controller design.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100359"},"PeriodicalIF":1.8,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925980","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}
Stem cells play a crucial role in biomedical research, offering remarkable potential for regenerative medicine, disease modeling, and drug discovery. Their ability to self-renew and differentiate into specialized cell types makes them essential for tissue repair and regeneration. This note explores a basic model of the differentiation/proliferation mechanisms while accounting for the maximum population size the environment can sustainably support due to limiting resources — i.e., the carrying capacity. Regulatory mechanisms affecting the proliferation rate are investigated using both deterministic and stochastic approaches. The deterministic analysis identifies regions of the parameter space that ensure a stable balance between stem and differentiated cells, while the stochastic approach provides valuable insights suggesting that a positive feedback on the proliferation rate leads to lower fluctuations in the accumulation of differentiated cells.
{"title":"A model of stem cell dynamics with carrying capacity: The role of feedback on proliferation rate","authors":"Alessandro Borri , Pasquale Palumbo , Abhyudai Singh","doi":"10.1016/j.ifacsc.2025.100358","DOIUrl":"10.1016/j.ifacsc.2025.100358","url":null,"abstract":"<div><div>Stem cells play a crucial role in biomedical research, offering remarkable potential for regenerative medicine, disease modeling, and drug discovery. Their ability to self-renew and differentiate into specialized cell types makes them essential for tissue repair and regeneration. This note explores a basic model of the differentiation/proliferation mechanisms while accounting for the maximum population size the environment can sustainably support due to limiting resources — i.e., the carrying capacity. Regulatory mechanisms affecting the proliferation rate are investigated using both deterministic and stochastic approaches. The deterministic analysis identifies regions of the parameter space that ensure a stable balance between stem and differentiated cells, while the stochastic approach provides valuable insights suggesting that a positive feedback on the proliferation rate leads to lower fluctuations in the accumulation of differentiated cells.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100358"},"PeriodicalIF":1.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925979","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 : 2025-12-27DOI: 10.1016/j.ifacsc.2025.100357
Mohd Faizan, Mahdi Boukerdja, Anne Lise Gehin, Belkacem Ould Bouamama, Sumit Sood
Energy system resilience refers to the ability of systems to operate effectively during disruptive events. These disruptions occur when control mechanisms fail due to actuator saturation, triggered by faults or attacks with unpredictable behaviour. Maintaining system resilience relies on recovery control strategies. However, these strategies are often delayed, leading to severe system performance degradation. A novel indicator, Remaining Time to Recovery (RTTR), has been introduced in this work to address the delay in recovery control implementation. This indicator facilitates the implementation of the anticipatory recovery control strategies to address this delay. An innovative method for the online estimation of RTTR has been proposed, based on a hybrid approach that combines Bond Graph (BG) modelling and Machine Learning (ML). In the proposed work, the BG reference model interacts with system measurements and instantly estimates power losses caused by faults or attacks before the system’s performance is impacted. The ML layer, using linear regression (LR), processes the estimated power loss data to derive a prediction model of power loss evolution that is updated in real-time. RTTR is then predicted based on the initiation of power loss and the predicted evolution of that loss over time. The proposed methodology has been validated on a two-tank system using real-time Hardware-in-the-Loop (HIL) simulation with a Speedgoat target machine. The HIL simulations in different scenarios have been presented to demonstrate the reliability and accuracy of the proposed approach.
{"title":"Online estimation of remaining time to recovery to enhance resilience using bond graph based power loss estimation","authors":"Mohd Faizan, Mahdi Boukerdja, Anne Lise Gehin, Belkacem Ould Bouamama, Sumit Sood","doi":"10.1016/j.ifacsc.2025.100357","DOIUrl":"10.1016/j.ifacsc.2025.100357","url":null,"abstract":"<div><div>Energy system resilience refers to the ability of systems to operate effectively during disruptive events. These disruptions occur when control mechanisms fail due to actuator saturation, triggered by faults or attacks with unpredictable behaviour. Maintaining system resilience relies on recovery control strategies. However, these strategies are often delayed, leading to severe system performance degradation. A novel indicator, Remaining Time to Recovery (RTTR), has been introduced in this work to address the delay in recovery control implementation. This indicator facilitates the implementation of the anticipatory recovery control strategies to address this delay. An innovative method for the online estimation of RTTR has been proposed, based on a hybrid approach that combines Bond Graph (BG) modelling and Machine Learning (ML). In the proposed work, the BG reference model interacts with system measurements and instantly estimates power losses caused by faults or attacks before the system’s performance is impacted. The ML layer, using linear regression (LR), processes the estimated power loss data to derive a prediction model of power loss evolution that is updated in real-time. RTTR is then predicted based on the initiation of power loss and the predicted evolution of that loss over time. The proposed methodology has been validated on a two-tank system using real-time Hardware-in-the-Loop (HIL) simulation with a Speedgoat target machine. The HIL simulations in different scenarios have been presented to demonstrate the reliability and accuracy of the proposed approach.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100357"},"PeriodicalIF":1.8,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885138","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}
A central challenge in direct data-driven control design is to ensure constraint satisfaction and safe operation of the closed-loop system while maintaining certain performance. To address this, we propose a hierarchical data-driven control architecture for constrained linear time-invariant systems to track a given setpoint reference. The inner-loop consists of a model reference controller (MRC) synthesized directly from the noisy data, which ensures performance by attempting to match a user-specified reference model. The outer-loop is a robust model predictive control (RMPC), acting as a safety pre-filter which optimally modifies the reference signal given to the inner loop MRC, ensuring constraint satisfaction and improving overall tracking performance. Additionally, the RMPC scheme accounts for a potential mismatch between the achieved closed-loop and the desired reference model, in the case of imperfect matching by the inner loop controller. The effectiveness of the method is demonstrated via a numerical example.
{"title":"Direct data-driven model-reference control for constrained systems","authors":"Manas Mejari, Milad Banitalebi Dehkordi, Dario Piga","doi":"10.1016/j.ifacsc.2025.100355","DOIUrl":"10.1016/j.ifacsc.2025.100355","url":null,"abstract":"<div><div>A central challenge in direct data-driven control design is to ensure constraint satisfaction and safe operation of the closed-loop system while maintaining certain performance. To address this, we propose a hierarchical data-driven control architecture for constrained linear time-invariant systems to track a given setpoint reference. The inner-loop consists of a model reference controller (MRC) synthesized directly from the noisy data, which ensures performance by attempting to match a user-specified reference model. The outer-loop is a robust model predictive control (RMPC), acting as a safety pre-filter which optimally modifies the reference signal given to the inner loop MRC, ensuring constraint satisfaction and improving overall tracking performance. Additionally, the RMPC scheme accounts for a potential mismatch between the achieved closed-loop and the desired reference model, in the case of imperfect matching by the inner loop controller. The effectiveness of the method is demonstrated via a numerical example.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100355"},"PeriodicalIF":1.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885139","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 : 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":"2025-12-16","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 : 2025-12-15DOI: 10.1016/j.ifacsc.2025.100354
Shuvo Dev , Mehedi Hassan , Naruttam Kumar Roy , Rabiul Islam
This study examines the design of a resilient control strategy for an IEEE 8-bus power system with renewable integration. It makes use of sophisticated control techniques such as Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), Sector-Bounded LQG (SBLQG), and Norm-Bounded LQG (NBLQG). By correcting model errors, the major goal of this study is to increase the power system’s resilience while preserving respectable performance indicators. To evaluate the efficacy of each control strategy, a thorough comparison is carried out using pole-zero plots, Bode plots, time-domain specifications, robust analysis, and statistical analysis. According to the pole-zero analysis, all control strategies have poles that are located in the left half-plane; the SBLQG and NBLQG strategies have the most leftward pole placements, which is a sign of better stability. The gain margin and phase margin consistently rise with each approach, according to Bode plot research, while the gain crossover and phase crossover frequencies also slightly increase. The controller’s enhanced robustness is evident in the 9.63% gain margin increases for LQG, 55.29% for SBLQG, and 86.79% for NBLQG when compared to LQR. In terms of time-domain performance, a decrease in rise time, peak time, and settling time is noted, while the percentage overshoot progressively diminishes in the sequence of LQR, LQG, SBLQG, and NBLQG. The percentage decrement in settling time for the controllers compared to LQR is 24.73% for LQG, 93.23% for SBLQG, and 98.06% for NBLQG, further highlighting their enhanced performance. The largest negative Cohen’s d values are observed in the comparison between LQR and NBLQG, with −24.4618 for GM and −18.9984 for PM, indicating a significant performance disparity. The results show that NBLQG is the most robust control strategy, exhibiting a modest settling time decrement. This research contributes to the field by illustrating how robust control methods, particularly NBLQG, effectively mitigate the impact of model uncertainties, thereby enhancing power system stability and performance in the presence of inaccuracies.
{"title":"Optimal and robust control techniques for stability enhancement in a renewable integrated power system","authors":"Shuvo Dev , Mehedi Hassan , Naruttam Kumar Roy , Rabiul Islam","doi":"10.1016/j.ifacsc.2025.100354","DOIUrl":"10.1016/j.ifacsc.2025.100354","url":null,"abstract":"<div><div>This study examines the design of a resilient control strategy for an IEEE 8-bus power system with renewable integration. It makes use of sophisticated control techniques such as Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), Sector-Bounded LQG (SBLQG), and Norm-Bounded LQG (NBLQG). By correcting model errors, the major goal of this study is to increase the power system’s resilience while preserving respectable performance indicators. To evaluate the efficacy of each control strategy, a thorough comparison is carried out using pole-zero plots, Bode plots, time-domain specifications, robust analysis, and statistical analysis. According to the pole-zero analysis, all control strategies have poles that are located in the left half-plane; the SBLQG and NBLQG strategies have the most leftward pole placements, which is a sign of better stability. The gain margin and phase margin consistently rise with each approach, according to Bode plot research, while the gain crossover and phase crossover frequencies also slightly increase. The controller’s enhanced robustness is evident in the 9.63% gain margin increases for LQG, 55.29% for SBLQG, and 86.79% for NBLQG when compared to LQR. In terms of time-domain performance, a decrease in rise time, peak time, and settling time is noted, while the percentage overshoot progressively diminishes in the sequence of LQR, LQG, SBLQG, and NBLQG. The percentage decrement in settling time for the controllers compared to LQR is 24.73% for LQG, 93.23% for SBLQG, and 98.06% for NBLQG, further highlighting their enhanced performance. The largest negative Cohen’s d values are observed in the comparison between LQR and NBLQG, with −24.4618 for GM and −18.9984 for PM, indicating a significant performance disparity. The results show that NBLQG is the most robust control strategy, exhibiting a modest settling time decrement. This research contributes to the field by illustrating how robust control methods, particularly NBLQG, effectively mitigate the impact of model uncertainties, thereby enhancing power system stability and performance in the presence of inaccuracies.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100354"},"PeriodicalIF":1.8,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884546","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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