Pub Date : 2025-04-16DOI: 10.1016/j.ifacsc.2025.100307
Bao-Zhu Guo , Wen-Qing Wei
In this paper, we investigate the uniform convergence of a semi-discrete scheme for output regulation of a system governed by a one-dimensional wave equation. The disturbances and reference signals stem from an exosystem, infiltrating the system through all channels. The exponential convergence of the continuous partial differential equation (PDE) system is firstly established using the Lyapunov functional approach. Utilizing the order reduction approach, we develop a semi-discrete finite difference scheme for the continuous PDE closed-loop system and demonstrate that this semi-discrete scheme exhibits uniform internal exponential stability, regardless of the step size, in complete alignment with its PDE counterpart. Consequently, the tracking errors for the discrete systems exhibit uniform exponential convergence.
{"title":"Uniform convergence of semi-discrete scheme for output regulation of 1-D wave equation","authors":"Bao-Zhu Guo , Wen-Qing Wei","doi":"10.1016/j.ifacsc.2025.100307","DOIUrl":"10.1016/j.ifacsc.2025.100307","url":null,"abstract":"<div><div>In this paper, we investigate the uniform convergence of a semi-discrete scheme for output regulation of a system governed by a one-dimensional wave equation. The disturbances and reference signals stem from an exosystem, infiltrating the system through all channels. The exponential convergence of the continuous partial differential equation (PDE) system is firstly established using the Lyapunov functional approach. Utilizing the order reduction approach, we develop a semi-discrete finite difference scheme for the continuous PDE closed-loop system and demonstrate that this semi-discrete scheme exhibits uniform internal exponential stability, regardless of the step size, in complete alignment with its PDE counterpart. Consequently, the tracking errors for the discrete systems exhibit uniform exponential convergence.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100307"},"PeriodicalIF":1.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848150","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 : 2025-04-08DOI: 10.1016/j.ifacsc.2025.100306
Marco Moran-Armenta , Jorge Montoya-Cháirez , Francisco G. Rossomando , Emanuel Slawiñski , Vicente Mut , Fernando A. Chicaiza , Javier Moreno-Valenzuela
This research proposes an innovative approach to improve the performance of regulation control systems in manipulators by combining PID control with gravitational compensation using neural networks. In this work, a modified PID control structure that incorporates a gravitational compensation term given by a neural network is introduced, thus allowing a more precise and adaptive response to gravitational and dynamic perturbations of the system. Furthermore, the controller’s performance is evaluated through real-time experiments in two manipulators, comparing its performance with the same structure, one without integral action, another without neural compensation and the last one assuming that the gravity vector is known. The results show a significant improvement in system regulation accuracy, demonstrating the proposed controller’s effectiveness.
{"title":"Neural networks meet PID control: Revolutionizing manipulator regulation with gravitational compensation","authors":"Marco Moran-Armenta , Jorge Montoya-Cháirez , Francisco G. Rossomando , Emanuel Slawiñski , Vicente Mut , Fernando A. Chicaiza , Javier Moreno-Valenzuela","doi":"10.1016/j.ifacsc.2025.100306","DOIUrl":"10.1016/j.ifacsc.2025.100306","url":null,"abstract":"<div><div>This research proposes an innovative approach to improve the performance of regulation control systems in manipulators by combining PID control with gravitational compensation using neural networks. In this work, a modified PID control structure that incorporates a gravitational compensation term given by a neural network is introduced, thus allowing a more precise and adaptive response to gravitational and dynamic perturbations of the system. Furthermore, the controller’s performance is evaluated through real-time experiments in two manipulators, comparing its performance with the same structure, one without integral action, another without neural compensation and the last one assuming that the gravity vector is known. The results show a significant improvement in system regulation accuracy, demonstrating the proposed controller’s effectiveness.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100306"},"PeriodicalIF":1.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829295","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 : 2025-03-29DOI: 10.1016/j.ifacsc.2025.100305
Ahmed Kamel, Ramin Esmzad, Nariman Niknejad, Hamidreza Modares
Balancing the trade-off between venturing into unknowns (exploration for learning) and optimizing outcomes within familiar grounds (exploitation for performance delivery) is a longstanding challenge in learning-enabled control systems. This is specifically challenging when the learning process starts with no data and rich data must be collected from the closed-loop system. This is in sharp contrast to the standard practice in data-driven control that assumes the availability of a priori rich collected open-loop data. To ensure that the closed-loop system delivers acceptable performance despite exploration for rich data collection in the context of linear quadratic regulator (LQR), we first formalize a linear matrix inequality (LMI) solution for an LQR problem that is regularized by the control entropy. Given available side information (e.g., a set that system parameters belong to), a conservative solution to the LQR can be found. To reduce the conservatism over time while ensuring an acceptable performance during learning, we present a set membership closed-loop system identification and integrate it with side information in solving the entropy-regularized LQR through Schur complement, along with the lossy S-procedure. We show that the presented set membership approach progressively improves the entropy-regularized LQR cost by shrinking the size of the set of system parameters. We also show that this is achieved while guaranteeing acceptable performance. An iterative algorithm is presented using the closed-loop set membership learning to progressively learn a new improved controller after every online data sample is collected by applying the current learned control policy. Simulation examples are provided to verify the effectiveness of the presented results.
{"title":"Robust adaptive maximum-entropy linear quadratic regulator","authors":"Ahmed Kamel, Ramin Esmzad, Nariman Niknejad, Hamidreza Modares","doi":"10.1016/j.ifacsc.2025.100305","DOIUrl":"10.1016/j.ifacsc.2025.100305","url":null,"abstract":"<div><div>Balancing the trade-off between venturing into unknowns (exploration for learning) and optimizing outcomes within familiar grounds (exploitation for performance delivery) is a longstanding challenge in learning-enabled control systems. This is specifically challenging when the learning process starts with no data and rich data must be collected from the closed-loop system. This is in sharp contrast to the standard practice in data-driven control that assumes the availability of a priori rich collected open-loop data. To ensure that the closed-loop system delivers acceptable performance despite exploration for rich data collection in the context of linear quadratic regulator (LQR), we first formalize a linear matrix inequality (LMI) solution for an LQR problem that is regularized by the control entropy. Given available side information (e.g., a set that system parameters belong to), a conservative solution to the LQR can be found. To reduce the conservatism over time while ensuring an acceptable performance during learning, we present a set membership closed-loop system identification and integrate it with side information in solving the entropy-regularized LQR through Schur complement, along with the lossy S-procedure. We show that the presented set membership approach progressively improves the entropy-regularized LQR cost by shrinking the size of the set of system parameters. We also show that this is achieved while guaranteeing acceptable performance. An iterative algorithm is presented using the closed-loop set membership learning to progressively learn a new improved controller after every online data sample is collected by applying the current learned control policy. Simulation examples are provided to verify the effectiveness of the presented results.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100305"},"PeriodicalIF":1.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739293","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}
Solar photovoltaic (PV) and wind energy systems (WESs) are essential for sustainable power generation, yet their performance is hindered by dynamic environmental conditions and inherent non-linearities. This review comprehensively examines advancements in maximum power point tracking (MPPT) techniques, which are crucial for optimizing the efficiency of these systems. The primary goals of this study are to offer a comprehensive evaluation of different MPPT approaches such as conventional, soft computing and hybrid techniques for PV and WESs and evaluating their effectiveness under various environments; to compare these methods depend on important performance metrices including efficiency, complexity, tracking speed, accuracy, sensor requirements and efficient operation, providing a detailed analysis for practical applications; to analyse technical and economic challenges related to MPPT deployment and provide the directions for future study to improve reliability and cost effectiveness of the system by highlighting the gaps in existing studies; and to emphasize the significance of hybrid approaches to achieve enhanced accuracy and faster tracking. By providing a detailed performance analysis and discussing the strengths and weaknesses of each method, this paper aims to guide the development of more efficient and cost-effective solutions, ultimately enhancing the sustainability and reliability of renewable energy technologies.
{"title":"Global peak operation of solar photovoltaic and wind energy systems: Current trends and innovations in enhanced optimization control techniques","authors":"Saranya Pulenthirarasa , Priya Ranjan Satpathy , Vigna K. Ramachandaramurthy , Agileswari Ramasamy , Arulampalam Atputharajah , Thurga R. Radha Krishnan","doi":"10.1016/j.ifacsc.2025.100304","DOIUrl":"10.1016/j.ifacsc.2025.100304","url":null,"abstract":"<div><div>Solar photovoltaic (PV) and wind energy systems (WESs) are essential for sustainable power generation, yet their performance is hindered by dynamic environmental conditions and inherent non-linearities. This review comprehensively examines advancements in maximum power point tracking (MPPT) techniques, which are crucial for optimizing the efficiency of these systems. The primary goals of this study are to offer a comprehensive evaluation of different MPPT approaches such as conventional, soft computing and hybrid techniques for PV and WESs and evaluating their effectiveness under various environments; to compare these methods depend on important performance metrices including efficiency, complexity, tracking speed, accuracy, sensor requirements and efficient operation, providing a detailed analysis for practical applications; to analyse technical and economic challenges related to MPPT deployment and provide the directions for future study to improve reliability and cost effectiveness of the system by highlighting the gaps in existing studies; and to emphasize the significance of hybrid approaches to achieve enhanced accuracy and faster tracking. By providing a detailed performance analysis and discussing the strengths and weaknesses of each method, this paper aims to guide the development of more efficient and cost-effective solutions, ultimately enhancing the sustainability and reliability of renewable energy technologies.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100304"},"PeriodicalIF":1.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768578","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 : 2025-03-24DOI: 10.1016/j.ifacsc.2025.100303
Sana BenKhaled , Cédric Delattre , Bessem Bhiri , Michel Zasadzinski , Kamel Abderrahim
This paper deals with the finite-time boundedness of an important class of hybrid systems, namely piecewise affine (PWA) systems. The main results in this article are sufficient conditions for finite-time boundedness and finite-time stabilization of PWA systems. Our approach uses a Lyapunov-like function and the S-procedure to obtain these conditions which are formulated in terms of LMIs. A numerical example illustrates the effectiveness of the proposed approach.
{"title":"Finite-time boundedness of piecewise affine systems","authors":"Sana BenKhaled , Cédric Delattre , Bessem Bhiri , Michel Zasadzinski , Kamel Abderrahim","doi":"10.1016/j.ifacsc.2025.100303","DOIUrl":"10.1016/j.ifacsc.2025.100303","url":null,"abstract":"<div><div>This paper deals with the finite-time boundedness of an important class of hybrid systems, namely piecewise affine (PWA) systems. The main results in this article are sufficient conditions for finite-time boundedness and finite-time stabilization of PWA systems. Our approach uses a Lyapunov-like function and the S-procedure to obtain these conditions which are formulated in terms of LMIs. A numerical example illustrates the effectiveness of the proposed approach.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100303"},"PeriodicalIF":1.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683560","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 : 2025-03-20DOI: 10.1016/j.ifacsc.2025.100302
Patricio Luppi , Lautaro Braccia , David Zumoffen
This paper presents the design of a plug-in module to address the problem of controller reconfiguration in industrial processes. The proposal is based on a multi-controller switching philosophy, where the modification of an interpolation signal defines the combination of the control actions of each controller. The contribution is based on the integration of two methodologies. On the one hand, a multivariable feedback control design approach, using the concepts of control allocation and measurement combination. On the other hand, the mapping of a set of linear stabilizing controllers onto a multi-controller, based on the Q-parameter from the Youla-Kucera theory. In this context, the set of controllers can be designed independently. Moreover, the stability is guaranteed subject to an arbitrary switching between different stabilizing controllers. The procedure is evaluated by considering two relevant scenarios of control reconfiguration: 1- a complete modification of the input–output pairing, and 2- the replacement of a classical controller with a new advanced control strategy. Based on the computational simulation of two case studies from the literature, it is shown that the plug-in module carries out the reconfiguration of the control structure, improving the dynamic performance and ensuring the stability of the system. The design is based on the nominal controller, which is not modified during the reconfiguration process. In addition, it can be easily implemented online, connected to input–output terminals of the existing controller.
{"title":"Plug-in module for controller reconfiguration based on latent variables and the Youla-Kucera parameterization","authors":"Patricio Luppi , Lautaro Braccia , David Zumoffen","doi":"10.1016/j.ifacsc.2025.100302","DOIUrl":"10.1016/j.ifacsc.2025.100302","url":null,"abstract":"<div><div>This paper presents the design of a plug-in module to address the problem of controller reconfiguration in industrial processes. The proposal is based on a multi-controller switching philosophy, where the modification of an interpolation signal defines the combination of the control actions of each controller. The contribution is based on the integration of two methodologies. On the one hand, a multivariable feedback control design approach, using the concepts of control allocation and measurement combination. On the other hand, the mapping of a set of linear stabilizing controllers onto a multi-controller, based on the Q-parameter from the Youla-Kucera theory. In this context, the set of controllers can be designed independently. Moreover, the stability is guaranteed subject to an arbitrary switching between different stabilizing controllers. The procedure is evaluated by considering two relevant scenarios of control reconfiguration: 1- a complete modification of the input–output pairing, and 2- the replacement of a classical controller with a new advanced control strategy. Based on the computational simulation of two case studies from the literature, it is shown that the plug-in module carries out the reconfiguration of the control structure, improving the dynamic performance and ensuring the stability of the system. The design is based on the nominal controller, which is not modified during the reconfiguration process. In addition, it can be easily implemented online, connected to input–output terminals of the existing controller.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100302"},"PeriodicalIF":1.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698000","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 : 2025-03-18DOI: 10.1016/j.ifacsc.2025.100301
Dola Sinha , Mou Das Mahapatra , Sucharita Pal , Saibal Majumder , Sovan Bhattacharya , Chandan Bandyopadhyay
The increasing demand for renewable energy integration has led to the development of advanced converter strategies to manage the inherent variability of renewable power sources. This paper presents a high-performance interleaved boost converter regulated by a fractional-order proportional-integral (FoPI) controller to ensure stable output voltage and power delivery under fluctuating input and load conditions. The FoPI controller parameters, including gains and fractional order, are optimized using particle swarm optimization (PSO) with the integral absolute error (IAE) as the objective function. The primary objective is to enhance the system’s robustness against input voltage variations and load disturbances. The proposed PSO-FoPI controller is tested under different operating scenarios: (i) a fixed input of 150 V, (ii) an input variation from 150 V to 350 V, and (iii) a fixed 200 V input with output power demand variations between 8 kW and 12.25 kW. Also sensitivity analysis with changing parameter values of the converter and inclusion of step and ramp input disturbances, the performance of the controller is evaluated. MATLAB/Simulink simulations demonstrate that the PSO-FoPI controller effectively maintains the desired 400 V output and an average power of 10 kW while reducing transient effects and harmonic distortions. Comparative analysis with PI controller, tuned via Ziegler–Nichols and PSO techniques, highlights the superior performance of the proposed approach. The results confirm that the PSO-FoPI-controlled interleaved boost converter enhances stability and efficiency, making it well-suited for real-time applications utilizing renewable power sources.
{"title":"Adaptation of fractional-order PI controller for a variable input interleaved DC–DC boost converter using particle swarm optimization with parametric variation","authors":"Dola Sinha , Mou Das Mahapatra , Sucharita Pal , Saibal Majumder , Sovan Bhattacharya , Chandan Bandyopadhyay","doi":"10.1016/j.ifacsc.2025.100301","DOIUrl":"10.1016/j.ifacsc.2025.100301","url":null,"abstract":"<div><div>The increasing demand for renewable energy integration has led to the development of advanced converter strategies to manage the inherent variability of renewable power sources. This paper presents a high-performance interleaved boost converter regulated by a fractional-order proportional-integral (FoPI) controller to ensure stable output voltage and power delivery under fluctuating input and load conditions. The FoPI controller parameters, including gains and fractional order, are optimized using particle swarm optimization (PSO) with the integral absolute error (IAE) as the objective function. The primary objective is to enhance the system’s robustness against input voltage variations and load disturbances. The proposed PSO-FoPI controller is tested under different operating scenarios: (i) a fixed input of 150 V, (ii) an input variation from 150 V to 350 V, and (iii) a fixed 200 V input with output power demand variations between 8 kW and 12.25 kW. Also sensitivity analysis with changing parameter values of the converter and inclusion of step and ramp input disturbances, the performance of the controller is evaluated. MATLAB/Simulink simulations demonstrate that the PSO-FoPI controller effectively maintains the desired 400 V output and an average power of 10 kW while reducing transient effects and harmonic distortions. Comparative analysis with PI controller, tuned via Ziegler–Nichols and PSO techniques, highlights the superior performance of the proposed approach. The results confirm that the PSO-FoPI-controlled interleaved boost converter enhances stability and efficiency, making it well-suited for real-time applications utilizing renewable power sources.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100301"},"PeriodicalIF":1.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683562","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 : 2025-03-01DOI: 10.1016/j.ifacsc.2025.100300
Pouria Tooranjipour, Bahare Kiumarsi
This paper introduces a novel approach for designing safe optimal controllers that avoid destructive conflicts between safety and performance in a large domain of the system’s operation. Designing computationally tractable feedback controllers that respect safety for a given set is impossible in general. The best one can do in this case is to maximize the region contained in the safe set that respects both safety and optimality. To this end, our key contribution lies in constructing a safe optimal domain of attraction (DoA) that ensures optimal convergence of the system’s trajectories to the origin without violating safety. To accomplish this, we leverage the concept of the relaxed Hamilton–Jacobi–Bellman (HJB) equation, which allows us to learn the most permissive control barrier certificates (CBCs) with a maximum-volume conflict-free set by solving a tractable optimization problem. To enhance computational efficiency, we present an innovative sum-of-squares (SOS)-based algorithm, breaking down the optimization problem into smaller SOS programs at each iteration. To alleviate the need for the system model to solve these SOS optimizations, an SOS-based off-policy reinforcement learning (RL) method is presented. This off-policy learning approach enables the evaluation of a target policy distinct from the behavior policy used for data collection, ensuring safe exploration under mild assumptions. In the end, the simulation results are given to show the efficacy of the proposed method.
{"title":"Learning optimal safety certificates for unknown nonlinear control systems","authors":"Pouria Tooranjipour, Bahare Kiumarsi","doi":"10.1016/j.ifacsc.2025.100300","DOIUrl":"10.1016/j.ifacsc.2025.100300","url":null,"abstract":"<div><div>This paper introduces a novel approach for designing safe optimal controllers that avoid destructive conflicts between safety and performance in a large domain of the system’s operation. Designing computationally tractable feedback controllers that respect safety for a given set is impossible in general. The best one can do in this case is to maximize the region contained in the safe set that respects both safety and optimality. To this end, our key contribution lies in constructing a safe optimal domain of attraction (DoA) that ensures optimal convergence of the system’s trajectories to the origin without violating safety. To accomplish this, we leverage the concept of the relaxed Hamilton–Jacobi–Bellman (HJB) equation, which allows us to learn the most permissive control barrier certificates (CBCs) with a maximum-volume conflict-free set by solving a tractable optimization problem. To enhance computational efficiency, we present an innovative sum-of-squares (SOS)-based algorithm, breaking down the optimization problem into smaller SOS programs at each iteration. To alleviate the need for the system model to solve these SOS optimizations, an SOS-based off-policy reinforcement learning (RL) method is presented. This off-policy learning approach enables the evaluation of a target policy distinct from the behavior policy used for data collection, ensuring safe exploration under mild assumptions. In the end, the simulation results are given to show the efficacy of the proposed method.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"31 ","pages":"Article 100300"},"PeriodicalIF":1.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562770","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 : 2025-02-18DOI: 10.1016/j.ifacsc.2025.100299
S.K. Laha
Estimating transfer functions from sampled input–output data is a critical task in system identification. Traditional approaches, such as least square optimization, often result in dense parameter estimates, which can be less interpretable and computationally intensive. This paper introduces a novel method for transfer function estimation by applying the Least Absolute Shrinkage and Selection Operator (LASSO), which promotes sparsity in the identified coefficients. The proposed approach enables sparse identification of both the numerator and denominator coefficients of the transfer function. The efficacy of the method is demonstrated through numerical experiments and application to the estimation of the natural frequencies of a turbine blade from its impulse response. By leveraging LASSO, we achieve a more parsimonious and interpretable model that captures the essential dynamics of the system. The results highlight the advantages of sparse modelling in system identification and its potential for improving the understanding and prediction of complex mechanical systems.
{"title":"A sparse approach to transfer function estimation via Least Absolute Shrinkage and Selection Operator","authors":"S.K. Laha","doi":"10.1016/j.ifacsc.2025.100299","DOIUrl":"10.1016/j.ifacsc.2025.100299","url":null,"abstract":"<div><div>Estimating transfer functions from sampled input–output data is a critical task in system identification. Traditional approaches, such as least square optimization, often result in dense parameter estimates, which can be less interpretable and computationally intensive. This paper introduces a novel method for transfer function estimation by applying the Least Absolute Shrinkage and Selection Operator (LASSO), which promotes sparsity in the identified coefficients. The proposed approach enables sparse identification of both the numerator and denominator coefficients of the transfer function. The efficacy of the method is demonstrated through numerical experiments and application to the estimation of the natural frequencies of a turbine blade from its impulse response. By leveraging LASSO, we achieve a more parsimonious and interpretable model that captures the essential dynamics of the system. The results highlight the advantages of sparse modelling in system identification and its potential for improving the understanding and prediction of complex mechanical systems.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"31 ","pages":"Article 100299"},"PeriodicalIF":1.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445842","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 : 2025-02-13DOI: 10.1016/j.ifacsc.2025.100298
Matteo Sangiorgio, Giorgio Guariso
Selecting a suitable dataset to develop a data-based forecasting model is often problematic. This is particularly important in the case of air pollution, where concentration measures are scattered over large areas. On the one hand, the classical approach creates a single-station (local) forecasting model using only the data collected at the same station. This guarantees a training dataset that considers all the site’s specific characteristics. On the other hand, these data may be limited and not sufficient to develop a robust predictor. Thus, one may use data from other stations to complement the dataset or develop a unique model considering all the data available within a region/domain. While this approach may be prone to filtering high variations, it may consider information on peculiar episodes that have not occurred in the past to a specific station. This paper discusses the topic of air pollution forecasting using the example of several stations in the Padana Plain, Northern Italy. Local forecasting models are developed using LSTM neural networks for nitrogen dioxide and ozone and hourly data from 2010 to 2023 and then compared with regional models. All these models perform extremely well under various regression-based and classification-based performance indicators, except for a few sites with peculiar characteristics that can be considered at the border of the information domain.
{"title":"Local vs regional neural air pollution forecasting models","authors":"Matteo Sangiorgio, Giorgio Guariso","doi":"10.1016/j.ifacsc.2025.100298","DOIUrl":"10.1016/j.ifacsc.2025.100298","url":null,"abstract":"<div><div>Selecting a suitable dataset to develop a data-based forecasting model is often problematic. This is particularly important in the case of air pollution, where concentration measures are scattered over large areas. On the one hand, the classical approach creates a single-station (local) forecasting model using only the data collected at the same station. This guarantees a training dataset that considers all the site’s specific characteristics. On the other hand, these data may be limited and not sufficient to develop a robust predictor. Thus, one may use data from other stations to complement the dataset or develop a unique model considering all the data available within a region/domain. While this approach may be prone to filtering high variations, it may consider information on peculiar episodes that have not occurred in the past to a specific station. This paper discusses the topic of air pollution forecasting using the example of several stations in the Padana Plain, Northern Italy. Local forecasting models are developed using LSTM neural networks for nitrogen dioxide and ozone and hourly data from 2010 to 2023 and then compared with regional models. All these models perform extremely well under various regression-based and classification-based performance indicators, except for a few sites with peculiar characteristics that can be considered at the border of the information domain.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"31 ","pages":"Article 100298"},"PeriodicalIF":1.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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