Pub Date : 2025-09-01Epub Date: 2025-07-17DOI: 10.1016/j.ifacsc.2025.100322
Saba Askari Noghani, Paolo Scarabaggio, Raffaele Carli, Mariagrazia Dotoli
This paper presents a novel model predictive control framework for managing energy flow in smart parking infrastructures with renewable energy facilities, electric vehicles, and solar-powered electric vehicles. The proposed control framework minimizes the energy costs for the parking lot operators, ensuring the user-defined charge levels for vehicles at departure, and protecting the charging infrastructure during operation. Field validation on Lonsdale Street, Melbourne (Australia)—using real data on vehicle behavior, solar irradiance, and energy prices—shows significant grid load reduction even with partial solar production. Compared to a rule-based strategy, the MPC approach reduces operational costs by 15.32% and energy demand by 6.12%. Lastly, we show that the proposed framework is robust under forecast uncertainty, supporting its practical deployment in dynamic real-world environments.
{"title":"Predictive energy scheduling of smart parking infrastructure with solar-powered electric vehicles","authors":"Saba Askari Noghani, Paolo Scarabaggio, Raffaele Carli, Mariagrazia Dotoli","doi":"10.1016/j.ifacsc.2025.100322","DOIUrl":"10.1016/j.ifacsc.2025.100322","url":null,"abstract":"<div><div>This paper presents a novel model predictive control framework for managing energy flow in smart parking infrastructures with renewable energy facilities, electric vehicles, and solar-powered electric vehicles. The proposed control framework minimizes the energy costs for the parking lot operators, ensuring the user-defined charge levels for vehicles at departure, and protecting the charging infrastructure during operation. Field validation on Lonsdale Street, Melbourne (Australia)—using real data on vehicle behavior, solar irradiance, and energy prices—shows significant grid load reduction even with partial solar production. Compared to a rule-based strategy, the MPC approach reduces operational costs by 15.32% and energy demand by 6.12%. Lastly, we show that the proposed framework is robust under forecast uncertainty, supporting its practical deployment in dynamic real-world environments.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100322"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724955","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 : 2025-06-01Epub 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":"2025-06-01","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}
Pub Date : 2025-06-01Epub Date: 2025-05-10DOI: 10.1016/j.ifacsc.2025.100313
Vijay Kumar Singh, Jagannathan Sarangapani
Achieving consensus within a user-defined time frame for uncertain nonlinear systems is both crucial and challenging. To tackle this issue, we propose an adaptive consensus protocol that utilizes a radial basis function neural network to handle unknown nonlinearities and actuator faults. Unlike traditional finite-time or fixed-time consensus methods, our approach employs continuous, time-varying feedback to guarantee convergence within the desired time. The proposed strategy ensures that all closed-loop signals of the system remain bounded, achieving consensus within the prescribed time. The effectiveness of the proposed control strategy is demonstrated through a simulation example of phase synchronization in a power system.
{"title":"Prescribed-time fault-tolerant consensus for uncertain nonlinear multi-agent systems","authors":"Vijay Kumar Singh, Jagannathan Sarangapani","doi":"10.1016/j.ifacsc.2025.100313","DOIUrl":"10.1016/j.ifacsc.2025.100313","url":null,"abstract":"<div><div>Achieving consensus within a user-defined time frame for uncertain nonlinear systems is both crucial and challenging. To tackle this issue, we propose an adaptive consensus protocol that utilizes a radial basis function neural network to handle unknown nonlinearities and actuator faults. Unlike traditional finite-time or fixed-time consensus methods, our approach employs continuous, time-varying feedback to guarantee convergence within the desired time. The proposed strategy ensures that all closed-loop signals of the system remain bounded, achieving consensus within the prescribed time. The effectiveness of the proposed control strategy is demonstrated through a simulation example of phase synchronization in a power system.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100313"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943562","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}
In this paper, we introduce a new concept termed global temporal observability for continuous and discrete linear dynamic systems and explore its connection with the classical notion of observability. It is shown that, as a concept, global temporal observability is a generalization of the classical observability. However, as a feature of a dynamic system, global temporal observability is embedded into classical observability. The necessary condition for global temporal observability is presented. Four linear systems were considered to test the proposed concept. Since observability is a binary test, our results matched the results of classical observability analysis when appropriate basis functions are utilized. The advantages and disadvantages of the proposed concept are discussed. The main advantage of global temporal observability is that it restores the state function for the entire time duration in a single step that requires matrix inversion. It is shown that global temporal observability connects state reconstruction, differential equations, and observability concepts.
{"title":"Global temporal observability of linear dynamic systems","authors":"Altay Zhakatayev , Yuriy Rogovchenko , Matthias Pätzold","doi":"10.1016/j.ifacsc.2025.100312","DOIUrl":"10.1016/j.ifacsc.2025.100312","url":null,"abstract":"<div><div>In this paper, we introduce a new concept termed global temporal observability for continuous and discrete linear dynamic systems and explore its connection with the classical notion of observability. It is shown that, as a concept, global temporal observability is a generalization of the classical observability. However, as a feature of a dynamic system, global temporal observability is embedded into classical observability. The necessary condition for global temporal observability is presented. Four linear systems were considered to test the proposed concept. Since observability is a binary test, our results matched the results of classical observability analysis when appropriate basis functions are utilized. The advantages and disadvantages of the proposed concept are discussed. The main advantage of global temporal observability is that it restores the state function for the entire time duration in a single step that requires matrix inversion. It is shown that global temporal observability connects state reconstruction, differential equations, and observability concepts.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100312"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928557","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}
<div><div>Air pollution affects 91% of the global population, causing approximately 4.2 million deaths annually, according to the World Health Organization. This study presents a comprehensive analysis of spatiotemporal air quality patterns in Ghaziabad, focusing on seasonal variations, aerosol characteristics, correlation analysis, machine learning-based modelling, sensitivity analysis, and short-term prediction of PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> and PM<sub>10</sub> concentrations using data from four monitoring stations (MS1, MS2, MS3, MS4). Alarming levels of PM<sub>10</sub> and PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span>, frequently exceeding permissible standards, were observed, particularly at MS2, where industrial activities led to an 81.29% exceedance rate for PM<sub>10</sub> with a maximum concentration increase of 447.23%. PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> concentrations at MS2 reached <span><math><mrow><mn>360</mn><mo>.</mo><mn>93</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m<sup>3</sup>, representing a 501.55% increase. Meteorological circumstances, particularly during winter, significantly increased pollution levels. SO<sub>2</sub> and ozone concentrations adhered to CPCB (Central Pollution Control Board) guidelines; nonetheless, winter months experienced a significant increase in overall pollutant levels. Positive correlations were identified between PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> and PM<sub>10</sub> with NO<sub>2</sub> (r <span><math><mo>=</mo></math></span> 0.54, r <span><math><mo>=</mo></math></span> 0.51), CO (r <span><math><mo>=</mo></math></span> 0.51, r <span><math><mo>=</mo></math></span> 0.45), and SO<sub>2</sub> (r <span><math><mo>=</mo></math></span> 0.18, r <span><math><mo>=</mo></math></span> 0.34), while negative correlations were noted with ozone (r <span><math><mo>=</mo></math></span> −0.02, r <span><math><mo>=</mo></math></span> −0.18), wind speed (r <span><math><mo>=</mo></math></span> −0.17, r <span><math><mo>=</mo></math></span> −0.20), and relative humidity (r <span><math><mo>=</mo></math></span> −0.08, r <span><math><mo>=</mo></math></span> −0.37). Solar radiation also showed a negative correlation (r <span><math><mo>=</mo></math></span> −0.32, r <span><math><mo>=</mo></math></span> −0.13). The study optimized predictive models for air quality forecasting using historical data. The XGBoost model outperformed others in predicting PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> and PM<sub>10</sub> concentrations, achieving the lowest Mean Absolute Error (MAE) and highest R<sup>2</sup> values (PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span>: MAE <span><math><mrow><mn>13</mn><mo>.</mo><mn>24</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m<su
世界卫生组织（World Health Organization）的数据显示，全球91%的人口受到空气污染的影响，每年造成约420万人死亡。本研究对加兹阿巴德的时空空气质量模式进行了全面分析，重点关注季节变化、气溶胶特征、相关性分析、基于机器学习的建模、敏感性分析，并利用四个监测站（MS1、MS2、MS3、MS4）的数据对PM2.5和PM10浓度进行了短期预测。PM10和PM2.5的警戒水平经常超过允许的标准，特别是在MS2，工业活动导致PM10超标率为81.29%，最大浓度增加了447.23%。PM2.5浓度达到360.93μg/m3，增长501.55%。气象环境，特别是冬季，大大增加了污染程度。二氧化硫和臭氧浓度符合中央污染控制委员会（CPCB）的准则；尽管如此，冬季的几个月总体污染物水平显著上升。PM2.5和PM10与NO2 （r = 0.54, r = 0.51）、CO （r = 0.51, r = 0.45）、SO2 （r = 0.18, r = 0.34）呈显著正相关，与臭氧（r = - 0.02, r = - 0.18）、风速（r = - 0.17, r = - 0.20）、相对湿度（r = - 0.08, r = - 0.37）呈显著负相关。太阳辐射也呈负相关（r = - 0.32, r = - 0.13）。该研究优化了利用历史数据预测空气质量的预测模型。XGBoost模型在预测PM2.5和PM10浓度方面优于其他模型，平均绝对误差（MAE）最低，R2最高（PM2.5: MAE 13.24μg/m3, R2 0.8960, PM10: MAE 27.46μg/m3, R2 0.8397）。灵敏度分析发现，PM10浓度对PM2.5水平的影响最大，对模型预测能力的贡献率约为63.56%，其次是太阳辐射（9.74%）和相对湿度（8.30%）。该模型准确预测了2023年的空气质量，具有较高的可靠性（2023年PM2.5: MAE 14.64μg/m3, R2 0.8850, PM10: MAE 27.66μg/m3, R2 0.8234）。这些可靠的短期预报对公共卫生规划和环境管理至关重要，有助于采取主动措施减轻污染水平，保障公众健康。可靠的预测有助于采取有针对性的行动，支持减少空气污染及其对人口的不利影响的政策决定。
{"title":"Air quality analysis and modelling of particulate matter (PM2.5 and PM10) of Ghaziabad city in India using Artificial Intelligence techniques","authors":"Patil Aashish Suhas, Aneesh Mathew, Chinthu Naresh","doi":"10.1016/j.ifacsc.2025.100315","DOIUrl":"10.1016/j.ifacsc.2025.100315","url":null,"abstract":"<div><div>Air pollution affects 91% of the global population, causing approximately 4.2 million deaths annually, according to the World Health Organization. This study presents a comprehensive analysis of spatiotemporal air quality patterns in Ghaziabad, focusing on seasonal variations, aerosol characteristics, correlation analysis, machine learning-based modelling, sensitivity analysis, and short-term prediction of PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> and PM<sub>10</sub> concentrations using data from four monitoring stations (MS1, MS2, MS3, MS4). Alarming levels of PM<sub>10</sub> and PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span>, frequently exceeding permissible standards, were observed, particularly at MS2, where industrial activities led to an 81.29% exceedance rate for PM<sub>10</sub> with a maximum concentration increase of 447.23%. PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> concentrations at MS2 reached <span><math><mrow><mn>360</mn><mo>.</mo><mn>93</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m<sup>3</sup>, representing a 501.55% increase. Meteorological circumstances, particularly during winter, significantly increased pollution levels. SO<sub>2</sub> and ozone concentrations adhered to CPCB (Central Pollution Control Board) guidelines; nonetheless, winter months experienced a significant increase in overall pollutant levels. Positive correlations were identified between PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> and PM<sub>10</sub> with NO<sub>2</sub> (r <span><math><mo>=</mo></math></span> 0.54, r <span><math><mo>=</mo></math></span> 0.51), CO (r <span><math><mo>=</mo></math></span> 0.51, r <span><math><mo>=</mo></math></span> 0.45), and SO<sub>2</sub> (r <span><math><mo>=</mo></math></span> 0.18, r <span><math><mo>=</mo></math></span> 0.34), while negative correlations were noted with ozone (r <span><math><mo>=</mo></math></span> −0.02, r <span><math><mo>=</mo></math></span> −0.18), wind speed (r <span><math><mo>=</mo></math></span> −0.17, r <span><math><mo>=</mo></math></span> −0.20), and relative humidity (r <span><math><mo>=</mo></math></span> −0.08, r <span><math><mo>=</mo></math></span> −0.37). Solar radiation also showed a negative correlation (r <span><math><mo>=</mo></math></span> −0.32, r <span><math><mo>=</mo></math></span> −0.13). The study optimized predictive models for air quality forecasting using historical data. The XGBoost model outperformed others in predicting PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span> and PM<sub>10</sub> concentrations, achieving the lowest Mean Absolute Error (MAE) and highest R<sup>2</sup> values (PM<span><math><msub><mrow></mrow><mrow><mi>2.5</mi></mrow></msub></math></span>: MAE <span><math><mrow><mn>13</mn><mo>.</mo><mn>24</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m<su","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100315"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204247","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":"2025-06-01","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}
As far as climate change is concerned, a recurrent question that is asked either at the government or a consumer level is: Why should I make efforts to reduce my CO emission levels whereas the others will not make these efforts? The present paper provides qualitative elements to this question when asked at the government level. More precisely, we assume that each country wants to maximize a tradeoff between an individual benefit brought by emitting CO and an economical damage due to climate change while being influenced by the reduction strategies of the other countries. The influence term is key for the analysis and enables more virtuous or cooperative behavior. Mathematically speaking, the contribution of this paper is: to propose an abstracted model of a complex decision problem; to integrate an abstracted model of climate change in the game of interest; to conduct the complete Nash equilibrium analysis of the proposed game (existence, uniqueness, expression, quantitative analysis); to conduct a detailed numerical analysis to quantify the discussed aspects such as the impact of cross-country imitation on the atmospheric global temperature in 2100.
{"title":"On the impact of cross-country imitation on climate change: A game-theoretical analysis","authors":"Bouchra Mroué , Anthony Couthures , Samson Lasaulce , Irinel-Constantin Morărescu","doi":"10.1016/j.ifacsc.2025.100309","DOIUrl":"10.1016/j.ifacsc.2025.100309","url":null,"abstract":"<div><div>As far as climate change is concerned, a recurrent question that is asked either at the government or a consumer level is: Why should I make efforts to reduce my CO<span><math><msub><mrow></mrow><mrow><mtext>2</mtext></mrow></msub></math></span> emission levels whereas the others will not make these efforts? The present paper provides qualitative elements to this question when asked at the government level. More precisely, we assume that each country wants to maximize a tradeoff between an individual benefit brought by emitting CO<span><math><msub><mrow></mrow><mrow><mtext>2</mtext></mrow></msub></math></span> and an economical damage due to climate change while being influenced by the reduction strategies of the other countries. The influence term is key for the analysis and enables more virtuous or cooperative behavior. Mathematically speaking, the contribution of this paper is: to propose an abstracted model of a complex decision problem; to integrate an abstracted model of climate change in the game of interest; to conduct the complete Nash equilibrium analysis of the proposed game (existence, uniqueness, expression, quantitative analysis); to conduct a detailed numerical analysis to quantify the discussed aspects such as the impact of cross-country imitation on the atmospheric global temperature in 2100.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100309"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891943","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 : 2025-06-01Epub Date: 2025-04-24DOI: 10.1016/j.ifacsc.2025.100310
Addie Irawan, Mohd Helmi Suid, R.M.T. Raja Ismail, Mohd Falfazli Mat Jusof, Mohd Iskandar Putra Azahar, Ahmad Nor Kasruddin Nasir
This paper addresses the challenge of enhancing pressure regulation in pneumatic servo systems, specifically for proportional valve-controlled double-acting pneumatic cylinders (PPVDC). A Hybrid Nonlinear Sine Cosine Algorithm (HNSCA) is proposed to optimize a Finite-Time Prescribed Performance Control (FT-PPC) integrated with a PID controller. The HNSCA combines the Nonlinear Sine Cosine Algorithm (NSCA) with Adaptive Safe Experimentation Dynamics (ASED) to fine-tune FT-PPC-PID parameters, achieving rapid transient response and system stability. Simulation results demonstrate significant improvements over other optimization variants like ESCA and ASCA, including a 96% faster rise time, 61.9% reduction in settling time, and 6.4% lower overshoot. Additionally, HNSCA reduced pressure oscillations by 25%–30%, lowered power consumption by 20%–30%, and achieved up to a 50% reduction in energy consumption under a 10 kg load. It also enhanced subsonic flow stability by 10%–15% under choked flow conditions. These advancements offer practical benefits for industries utilizing pneumatic systems, such as manufacturing and robotics, by providing more precise control, reducing energy costs, and extending equipment lifespan. The findings highlight the effectiveness of the proposed approach in error minimization and long-term stability for pneumatic servo systems.
{"title":"Hybrid adaptive Sine Cosine Algorithm with Finite-Time Prescribed Performance PID Control for pneumatic servo systems","authors":"Addie Irawan, Mohd Helmi Suid, R.M.T. Raja Ismail, Mohd Falfazli Mat Jusof, Mohd Iskandar Putra Azahar, Ahmad Nor Kasruddin Nasir","doi":"10.1016/j.ifacsc.2025.100310","DOIUrl":"10.1016/j.ifacsc.2025.100310","url":null,"abstract":"<div><div>This paper addresses the challenge of enhancing pressure regulation in pneumatic servo systems, specifically for proportional valve-controlled double-acting pneumatic cylinders (PPVDC). A Hybrid Nonlinear Sine Cosine Algorithm (HNSCA) is proposed to optimize a Finite-Time Prescribed Performance Control (FT-PPC) integrated with a PID controller. The HNSCA combines the Nonlinear Sine Cosine Algorithm (NSCA) with Adaptive Safe Experimentation Dynamics (ASED) to fine-tune FT-PPC-PID parameters, achieving rapid transient response and system stability. Simulation results demonstrate significant improvements over other optimization variants like ESCA and ASCA, including a 96% faster rise time, 61.9% reduction in settling time, and 6.4% lower overshoot. Additionally, HNSCA reduced pressure oscillations by 25%–30%, lowered power consumption by 20%–30%, and achieved up to a 50% reduction in energy consumption under a 10 kg load. It also enhanced subsonic flow stability by 10%–15% under choked flow conditions. These advancements offer practical benefits for industries utilizing pneumatic systems, such as manufacturing and robotics, by providing more precise control, reducing energy costs, and extending equipment lifespan. The findings highlight the effectiveness of the proposed approach in error minimization and long-term stability for pneumatic servo systems.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100310"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878484","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}
In this paper, we propose a bottom-up approach for designing sparse static output-feedback controllers for large-scale systems. Starting from an existing sparse controller, we iteratively add feedback channels using a gradient-based predictor, optimizing the closed-loop norm within a predefined budget constraint. The proposed method significantly reduces the computational burden compared to traditional top-down approaches, which rely on pruning centralized controllers. We prove the convergence of our method and demonstrate its scalability through benchmarks, achieving comparable or better performance with significantly less computation time. This approach paves the way for efficient and scalable control design in distributed systems.
{"title":"A bottom-up approach for searching for sparse controllers with a budget","authors":"Vasanth Reddy , Suat Gumussoy , Almuatazbellah Boker , Hoda Eldardiry","doi":"10.1016/j.ifacsc.2025.100308","DOIUrl":"10.1016/j.ifacsc.2025.100308","url":null,"abstract":"<div><div>In this paper, we propose a bottom-up approach for designing sparse static output-feedback controllers for large-scale systems. Starting from an existing sparse controller, we iteratively add feedback channels using a gradient-based predictor, optimizing the closed-loop <span><math><mrow><msub><mrow><mi>H</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>−</mo></mrow></math></span>norm within a predefined budget constraint. The proposed method significantly reduces the computational burden compared to traditional top-down approaches, which rely on pruning centralized controllers. We prove the convergence of our method and demonstrate its scalability through benchmarks, achieving comparable or better performance with significantly less computation time. This approach paves the way for efficient and scalable control design in distributed systems.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100308"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189379","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 : 2025-06-01Epub 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":"2025-06-01","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}
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