Pub Date : 2026-01-02DOI: 10.1016/j.jfranklin.2025.108397
Hanyu Qian, Wenzheng Yuan, Bing Xiao
This paper addresses the critical yet understudied problem of initial spatial configuration in orbital pursuit-evasion games (OPEG). While existing research mainly focuses on in-game decision-making, the optimal pre-game deployment of cooperative satellites remains largely unexplored. To bridge this gap, we first establish a mathematical model for the configuration optimization problem and then develop a novel Dynamic Directional Competition Swarm Optimizer (DDCSO) to solve it. Numerical simulations demonstrate that the proposed approach improves the OPEG winning rate by over 12.30% compared to methods without optimized initial configuration, while the optimization accuracy of the DDCSO is improved by more than 3.62% relative to there optimizes. These results verify both the effectiveness of the proposed configuration design method and the superior performance of the DDCSO.
{"title":"Intelligent initial configuration design for connected satellites control in pursuit-evasion games via dynamic directional competitive swarm optimizer","authors":"Hanyu Qian, Wenzheng Yuan, Bing Xiao","doi":"10.1016/j.jfranklin.2025.108397","DOIUrl":"10.1016/j.jfranklin.2025.108397","url":null,"abstract":"<div><div>This paper addresses the critical yet understudied problem of initial spatial configuration in orbital pursuit-evasion games (OPEG). While existing research mainly focuses on in-game decision-making, the optimal pre-game deployment of cooperative satellites remains largely unexplored. To bridge this gap, we first establish a mathematical model for the configuration optimization problem and then develop a novel Dynamic Directional Competition Swarm Optimizer (DDCSO) to solve it. Numerical simulations demonstrate that the proposed approach improves the OPEG winning rate by over 12.30% compared to methods without optimized initial configuration, while the optimization accuracy of the DDCSO is improved by more than 3.62% relative to there optimizes. These results verify both the effectiveness of the proposed configuration design method and the superior performance of the DDCSO.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 2","pages":"Article 108397"},"PeriodicalIF":4.2,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.jfranklin.2025.108380
Shuo Gao , Bo-Chao Zheng , Hanqing Qu
To address the challenge of quadrotor UAVs failing to complete their tasks due to abrupt external disturbances and actuator failures, this paper proposes a nonlinear recursive sliding mode control (NRSMC) strategy integrated with an adaptive disturbance estimator (ADE) within the delta operator framework. First, an adaptive disturbance estimator is designed for the quadrotor UAV system in the delta operator framework. This estimator is capable of estimating both the total disturbance and its derivatives, which enhances the accuracy and adaptability of disturbance estimation, particularly in the presence of abrupt disturbances. Second, a nonlinear recursive sliding mode control strategy is designed. By combining the disturbance estimation results, the control law ensures the system converges quickly and smoothly. Finally, simulation experiments are conducted to validate the proposed method. The results show that the approach can effectively compensate for abrupt disturbances and actuator failures. This allows the quadrotor UAV to maintain stable trajectory tracking and successfully complete its tasks when facing abrupt disturbances and actuator failures.
{"title":"Sliding mode fault-tolerant control of quadrotor UAV based on adaptive disturbance estimator under delta operator framework","authors":"Shuo Gao , Bo-Chao Zheng , Hanqing Qu","doi":"10.1016/j.jfranklin.2025.108380","DOIUrl":"10.1016/j.jfranklin.2025.108380","url":null,"abstract":"<div><div>To address the challenge of quadrotor UAVs failing to complete their tasks due to abrupt external disturbances and actuator failures, this paper proposes a nonlinear recursive sliding mode control (NRSMC) strategy integrated with an adaptive disturbance estimator (ADE) within the delta operator framework. First, an adaptive disturbance estimator is designed for the quadrotor UAV system in the delta operator framework. This estimator is capable of estimating both the total disturbance and its derivatives, which enhances the accuracy and adaptability of disturbance estimation, particularly in the presence of abrupt disturbances. Second, a nonlinear recursive sliding mode control strategy is designed. By combining the disturbance estimation results, the control law ensures the system converges quickly and smoothly. Finally, simulation experiments are conducted to validate the proposed method. The results show that the approach can effectively compensate for abrupt disturbances and actuator failures. This allows the quadrotor UAV to maintain stable trajectory tracking and successfully complete its tasks when facing abrupt disturbances and actuator failures.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 2","pages":"Article 108380"},"PeriodicalIF":4.2,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper tackles the challenge of identifying parameters in a linear auto-regressive exogenous (ARX) model, particularly when using multiple sensors with unknown noise variances. To address this, we have expanded the multitask maximum likelihood (MTML) identification method into a robust multitask expectation maximization (MEM) approach. This new method not only estimates the model parameters and unknown noise variances but also determines analytical weights simultaneously. Our proposed MEM method outperforms the MTML in terms of accuracy, benefiting from the integration of multiple unknown noise variances and its adaptability to fluctuating noise conditions. The effectiveness of the MEM method is demonstrated through a numerical example and a case study involving a continuous fermentor, showcasing its superior identification capabilities and adaptability.
{"title":"Identification for ARX models with multiple unknown noise variances: A multitask expectation maximization approach","authors":"Yixuan Chu , Xiaojing Ping , Shunyi Zhao , Chengxi Zhang , Ruomu Tan","doi":"10.1016/j.jfranklin.2025.108399","DOIUrl":"10.1016/j.jfranklin.2025.108399","url":null,"abstract":"<div><div>This paper tackles the challenge of identifying parameters in a linear auto-regressive exogenous (ARX) model, particularly when using multiple sensors with unknown noise variances. To address this, we have expanded the multitask maximum likelihood (MTML) identification method into a robust multitask expectation maximization (MEM) approach. This new method not only estimates the model parameters and unknown noise variances but also determines analytical weights simultaneously. Our proposed MEM method outperforms the MTML in terms of accuracy, benefiting from the integration of multiple unknown noise variances and its adaptability to fluctuating noise conditions. The effectiveness of the MEM method is demonstrated through a numerical example and a case study involving a continuous fermentor, showcasing its superior identification capabilities and adaptability.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 4","pages":"Article 108399"},"PeriodicalIF":4.2,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.jfranklin.2025.108386
Le Liu, Shanghua Wu, Yinghao Xie, Yiming Fang
In this article, the issue of asymmetric prescribed performance tracking control is looked into regarding a class of nonlinear systems subject to unknown initial tracking condition and asymmetric performance constraint, and an innovative disturbance observer-based predefined-time performance constrained control strategy is developed. Firstly, through the construction of the predefined-time disturbance observer to achieve disturbance term suppression, and the estimation error converges to zero rapidly and accurately within a predefined time, without the need for a complex solving process to determine the convergence time. Subsequently, a new normalization function and two asymmetric performance functions related to the sign of the error are designed to achieve predefined performance metrics, significantly reducing the overshoot of error while overcoming the limitation that the initial error must be known. Secondly, based on the concept of predefined-time stability control theory, a disturbance observer-based predefined-time constrained controller is designed using the backstepping technique, while strictly specifying the convergence time of the tracking error. Furthermore, the practical predefined-time stability of the controlled system is proven using Lyapunov theory, and all the closed-loop signals are predefined-time bounded. Additionally, an improved predefined-time nonlinear filter is constructed to obtain the derivative of the virtual controller, simplifying the calculation burden of control system. Finally, the effectiveness and practicality of the proposed method are validated through simulations and dSPACE simulated experiments of a permanent magnet synchronous motor (PMSM) position system.
{"title":"Disturbance observer-based predefined-time filter control for a class of nonlinear systems subject to asymmetric performance constraint","authors":"Le Liu, Shanghua Wu, Yinghao Xie, Yiming Fang","doi":"10.1016/j.jfranklin.2025.108386","DOIUrl":"10.1016/j.jfranklin.2025.108386","url":null,"abstract":"<div><div>In this article, the issue of asymmetric prescribed performance tracking control is looked into regarding a class of nonlinear systems subject to unknown initial tracking condition and asymmetric performance constraint, and an innovative disturbance observer-based predefined-time performance constrained control strategy is developed. Firstly, through the construction of the predefined-time disturbance observer to achieve disturbance term suppression, and the estimation error converges to zero rapidly and accurately within a predefined time, without the need for a complex solving process to determine the convergence time. Subsequently, a new normalization function and two asymmetric performance functions related to the sign of the error are designed to achieve predefined performance metrics, significantly reducing the overshoot of error while overcoming the limitation that the initial error must be known. Secondly, based on the concept of predefined-time stability control theory, a disturbance observer-based predefined-time constrained controller is designed using the backstepping technique, while strictly specifying the convergence time of the tracking error. Furthermore, the practical predefined-time stability of the controlled system is proven using Lyapunov theory, and all the closed-loop signals are predefined-time bounded. Additionally, an improved predefined-time nonlinear filter is constructed to obtain the derivative of the virtual controller, simplifying the calculation burden of control system. Finally, the effectiveness and practicality of the proposed method are validated through simulations and dSPACE simulated experiments of a permanent magnet synchronous motor (PMSM) position system.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 2","pages":"Article 108386"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.jfranklin.2025.108377
Yonghong Lan, Shikang Zheng
Model-free adaptive control (MFAC) can provide advantages such as lower operational costs, higher scalability and easier implementation. However, how to further improve the controller performance and optimize the controller parameters is still an open problem. In this paper, a high-order pseudo-partial derivative estimation-based improved fractional-order model-free adaptive control (HOPPD-IFOMFAC) scheme and linear matrix inequality (LMI)-based parameters optimization method are proposed for a class of single-input single-output discrete-time nonlinear systems. Firstly, a compact form of fractional-order dynamic linearization model is used to equivalently describe the nonlinear system, which relates the variation of the output signal with the fractional-order variation of the input one. Considering more information of the previous time, a high-order pseudo-partial derivative parameter estimator algorithm is designed. Then, with the introduction of the observer, a control input criterion function, which includes the observer tracking error and the rate of its change is presented. On the base of this, the HOPPD-IFOMFAC scheme is derived, which not only utilizes more control knowledge of the previous time in the control law, but also uses more information of the previous time in the estimation algorithm, which can effectively improve control performance. Furthermore, some of the controller parameters are optimized by using the LMI convex optimization technique. Finally, two numerical examples demonstrate that the proposed HOPPD-IFOMFAC scheme can track the desired trajectory with improved convergence and tracking performance.
{"title":"Improved fractional model-free adaptive control and parameters optimization for a class of nonlinear discrete systems","authors":"Yonghong Lan, Shikang Zheng","doi":"10.1016/j.jfranklin.2025.108377","DOIUrl":"10.1016/j.jfranklin.2025.108377","url":null,"abstract":"<div><div>Model-free adaptive control (MFAC) can provide advantages such as lower operational costs, higher scalability and easier implementation. However, how to further improve the controller performance and optimize the controller parameters is still an open problem. In this paper, a high-order pseudo-partial derivative estimation-based improved fractional-order model-free adaptive control (HOPPD-IFOMFAC) scheme and linear matrix inequality (LMI)-based parameters optimization method are proposed for a class of single-input single-output discrete-time nonlinear systems. Firstly, a compact form of fractional-order dynamic linearization model is used to equivalently describe the nonlinear system, which relates the variation of the output signal with the fractional-order variation of the input one. Considering more information of the previous time, a high-order pseudo-partial derivative parameter estimator algorithm is designed. Then, with the introduction of the observer, a control input criterion function, which includes the observer tracking error and the rate of its change is presented. On the base of this, the HOPPD-IFOMFAC scheme is derived, which not only utilizes more control knowledge of the previous time in the control law, but also uses more information of the previous time in the estimation algorithm, which can effectively improve control performance. Furthermore, some of the controller parameters are optimized by using the LMI convex optimization technique. Finally, two numerical examples demonstrate that the proposed HOPPD-IFOMFAC scheme can track the desired trajectory with improved convergence and tracking performance.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 2","pages":"Article 108377"},"PeriodicalIF":4.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.jfranklin.2025.108379
Qiao Peng , Shoucheng Yuan , Yunliang Wang , Jun Cheng , Dan Zhang
This paper investigates the security control problem of two-dimensional (2-D) fuzzy switched systems (FSSs) subject to denial-of-service (DoS) attacks. Traditional approaches often rely on Markov transition probabilities to describe switching behaviors, yet such information is typically difficult to obtain in practice. To overcome this limitation, a novel framework based on nonhomogeneous sojourn probabilities is introduced, enabling realistic modeling of time-varying switching processes without requiring exact transition probabilities. Furthermore, frequency- and duration-constrained DoS attacks are incorporated into the system description, capturing the intermittent nature of practical attacks. Within this setting, a sliding mode control strategy is developed through the construction of a tailored sliding surface, ensuring robust performance despite cyber disruptions and external disturbances. By employing switched Lyapunov functions and the average dwell-time method, sufficient conditions are derived to guarantee asymptotic mean-square stability and prescribed noise attenuation. Simulation results validate the effectiveness of the proposed design, highlighting its superior robustness and practical applicability in securing 2-D FSSs against realistic network-induced threats.
{"title":"Sliding mode security control for 2-D fuzzy switched systems with nonhomogeneous sojourn probability strategy","authors":"Qiao Peng , Shoucheng Yuan , Yunliang Wang , Jun Cheng , Dan Zhang","doi":"10.1016/j.jfranklin.2025.108379","DOIUrl":"10.1016/j.jfranklin.2025.108379","url":null,"abstract":"<div><div>This paper investigates the security control problem of two-dimensional (2-D) fuzzy switched systems (FSSs) subject to denial-of-service (DoS) attacks. Traditional approaches often rely on Markov transition probabilities to describe switching behaviors, yet such information is typically difficult to obtain in practice. To overcome this limitation, a novel framework based on nonhomogeneous sojourn probabilities is introduced, enabling realistic modeling of time-varying switching processes without requiring exact transition probabilities. Furthermore, frequency- and duration-constrained DoS attacks are incorporated into the system description, capturing the intermittent nature of practical attacks. Within this setting, a sliding mode control strategy is developed through the construction of a tailored sliding surface, ensuring robust performance despite cyber disruptions and external disturbances. By employing switched Lyapunov functions and the average dwell-time method, sufficient conditions are derived to guarantee asymptotic mean-square stability and prescribed <span><math><msub><mi>H</mi><mi>∞</mi></msub></math></span> noise attenuation. Simulation results validate the effectiveness of the proposed design, highlighting its superior robustness and practical applicability in securing 2-D FSSs against realistic network-induced threats.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 2","pages":"Article 108379"},"PeriodicalIF":4.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-28DOI: 10.1016/j.jfranklin.2025.108354
N. Sakthivel, V. Rajkumar, S. Keerthna
This article is focused on the state estimation problem of nonlinear complex dynamical networks (CDNs) with the combination of Markovian switching parameters, exogenous disturbance, coupling delay and multiple cyber attacks. Precisely, the controller is designed with an event-triggered mechanism to obtain the synchronization of addressed CDNs, which reduces the burden of the communication channel and improves the implementation of bandwidth. Further, the event-triggered control technique is modeled with adversary attacks, namely, deception attacks and denial-of-service (DoS) attacks, which acquire the secure synchronization of the considered networks. By establishing a suitable Lyapunov–Krasovskii functional (LKF), novel adequate criteria have been obtained in the form of linear matrix inequalities (LMIs), which guarantee the secure synchronization of CDNs based on mixed H∞ and passivity performance. Ultimately, the obtained theoretical results are validated through numerical examples.
{"title":"Mixed H∞ and passivity based state estimation for Markovian switching complex dynamical networks with event-triggered control subject to multiple cyber attacks","authors":"N. Sakthivel, V. Rajkumar, S. Keerthna","doi":"10.1016/j.jfranklin.2025.108354","DOIUrl":"10.1016/j.jfranklin.2025.108354","url":null,"abstract":"<div><div>This article is focused on the state estimation problem of nonlinear complex dynamical networks (CDNs) with the combination of Markovian switching parameters, exogenous disturbance, coupling delay and multiple cyber attacks. Precisely, the controller is designed with an event-triggered mechanism to obtain the synchronization of addressed CDNs, which reduces the burden of the communication channel and improves the implementation of bandwidth. Further, the event-triggered control technique is modeled with adversary attacks, namely, deception attacks and denial-of-service (DoS) attacks, which acquire the secure synchronization of the considered networks. By establishing a suitable Lyapunov–Krasovskii functional (LKF), novel adequate criteria have been obtained in the form of linear matrix inequalities (LMIs), which guarantee the secure synchronization of CDNs based on mixed <em>H</em><sub>∞</sub> and passivity performance. Ultimately, the obtained theoretical results are validated through numerical examples.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 3","pages":"Article 108354"},"PeriodicalIF":4.2,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","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.jfranklin.2025.108376
Zhicheng Li, Xiaona Liu, Xiang Xu, Yang Wang
Model-based control methods need to identify the model of the system before being used in the actual system. Usually, the system identification method can not be calculated online. Thus, there are many difficulties in the application of model-based control methods, especially when the actual system’s parameters change slowly. Data-driven control methods can design the controller through the input and output data and do not need the system model as the premise. In this paper, the data-driven control methods are investigated to solve the control problem of linear slowly time-varying systems (LSTV systems). Since the parameters in the systems change slowly, in every small time interval, the LSTV system can be treated as a linear time-invariant system. According to the above fundamental assumption, and considering the small changes of the parameters within a short time, the data-driven robust control method of the time-invariant system is presented to solve the control problem for the LSTV system. Secondly, considering the cumulative effect of parameter errors as time lapses, a robust control method for time-invariant systems would not be effective anymore. Thus, we analyze the refreshed controller in two ways according to the Superposition principle for linear systems. On one hand, The fully refresh controller design method is presented. On the other hand, the incrementally refresh controller design method is proposed. The novelty of the proposed methods is that both of the methods are online algorithms, which can be refreshed properly before the original controller completely fails. At last, a special car inverted pendulum is proposed to illustrate the effectiveness of the presented methods.
{"title":"Data-driven controller synthesis for linear slowly time-varying systems","authors":"Zhicheng Li, Xiaona Liu, Xiang Xu, Yang Wang","doi":"10.1016/j.jfranklin.2025.108376","DOIUrl":"10.1016/j.jfranklin.2025.108376","url":null,"abstract":"<div><div>Model-based control methods need to identify the model of the system before being used in the actual system. Usually, the system identification method can not be calculated online. Thus, there are many difficulties in the application of model-based control methods, especially when the actual system’s parameters change slowly. Data-driven control methods can design the controller through the input and output data and do not need the system model as the premise. In this paper, the data-driven control methods are investigated to solve the control problem of linear slowly time-varying systems (LSTV systems). Since the parameters in the systems change slowly, in every small time interval, the LSTV system can be treated as a linear time-invariant system. According to the above fundamental assumption, and considering the small changes of the parameters within a short time, the data-driven robust control method of the time-invariant system is presented to solve the control problem for the LSTV system. Secondly, considering the cumulative effect of parameter errors as time lapses, a robust control method for time-invariant systems would not be effective anymore. Thus, we analyze the refreshed controller in two ways according to the Superposition principle for linear systems. On one hand, The fully refresh controller design method is presented. On the other hand, the incrementally refresh controller design method is proposed. The novelty of the proposed methods is that both of the methods are online algorithms, which can be refreshed properly before the original controller completely fails. At last, a special car inverted pendulum is proposed to illustrate the effectiveness of the presented methods.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 2","pages":"Article 108376"},"PeriodicalIF":4.2,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1016/j.jfranklin.2025.108331
Feng Peng , Xuebo Yang , Tong Wang
This paper investigates the problem of hierarchical optimization control for complex industrial systems. To address the challenges of infeasible setpoints between real-time optimization (RTO) and model predictive control (MPC) layers as well as the challenges of parametric uncertainties in the dynamic control, a hierarchical optimization control framework that integrates coordinated steady-state target optimization (SSTO) with adaptive MPC is proposed. First, a global coordination strategy based on genetic algorithm (GA) is developed to solve nonlinear SSTO problems and determine coordinated operating points. Second, an adaptive MPC scheme is designed, which incorporates an online parameter updating law together with a constrained MPC formulation for the estimated system. This design guarantees accurate tracking of the coordinated operating points while ensuring recursive feasibility and closed-loop stability. Simulation studies validate the effectiveness of the proposed framework, thereby exhibiting significant enhancements in economic performance, constraint feasibility, and robustness.
{"title":"Hierarchical optimization control for cascade industrial systems: A coordinated SSTO- and adaptive MPC-based approach","authors":"Feng Peng , Xuebo Yang , Tong Wang","doi":"10.1016/j.jfranklin.2025.108331","DOIUrl":"10.1016/j.jfranklin.2025.108331","url":null,"abstract":"<div><div>This paper investigates the problem of hierarchical optimization control for complex industrial systems. To address the challenges of infeasible setpoints between real-time optimization (RTO) and model predictive control (MPC) layers as well as the challenges of parametric uncertainties in the dynamic control, a hierarchical optimization control framework that integrates coordinated steady-state target optimization (SSTO) with adaptive MPC is proposed. First, a global coordination strategy based on genetic algorithm (GA) is developed to solve nonlinear SSTO problems and determine coordinated operating points. Second, an adaptive MPC scheme is designed, which incorporates an online parameter updating law together with a constrained MPC formulation for the estimated system. This design guarantees accurate tracking of the coordinated operating points while ensuring recursive feasibility and closed-loop stability. Simulation studies validate the effectiveness of the proposed framework, thereby exhibiting significant enhancements in economic performance, constraint feasibility, and robustness.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 2","pages":"Article 108331"},"PeriodicalIF":4.2,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-25DOI: 10.1016/j.jfranklin.2025.108378
Linqing Du, Jun Fu, Huan Li
A hybrid intelligent optimization method by improving particle swarm optimization and genetic algorithm (PSO-GA) is proposed for solving multi-objective optimal control problem (MOOCP) governed by ordinary differential equations (ODEs) in nonlinear systems. This method combines the global search capability of heuristic algorithm and the rapid convergence of deterministic algorithm to generate approximately uniform Pareto front. Firstly, the MOOCP is converted into a single-objective optimal control problem (SOOCP) subject to inequality point constraints using an adaptive ε-constraint method [1]. Secondly, the enhanced PSO is combined with a two-criteria GA to locate the global optimal region of SOOCP. Thirdly, sequential quadratic programming (SQP) algorithm is applied in the global optimal region to achieve fast convergence to high local accuracy solutions satisfying optimality conditions. Finally, the effectiveness and superiority of the proposed method are ultimately validated through numerical simulations and comparative analyses against state-of-the-art algorithms. Specifically, the hybrid intelligent optimization algorithm shows a 7.65% improvement in hypervolume and a 46.60% reduction in spacing metric compared to the suboptimal algorithm.
{"title":"Hybrid intelligent multi-objective optimal control of ODE-constrained nonlinear systems","authors":"Linqing Du, Jun Fu, Huan Li","doi":"10.1016/j.jfranklin.2025.108378","DOIUrl":"10.1016/j.jfranklin.2025.108378","url":null,"abstract":"<div><div>A hybrid intelligent optimization method by improving particle swarm optimization and genetic algorithm (PSO-GA) is proposed for solving multi-objective optimal control problem (MOOCP) governed by ordinary differential equations (ODEs) in nonlinear systems. This method combines the global search capability of heuristic algorithm and the rapid convergence of deterministic algorithm to generate approximately uniform Pareto front. Firstly, the MOOCP is converted into a single-objective optimal control problem (SOOCP) subject to inequality point constraints using an adaptive ε-constraint method [1]. Secondly, the enhanced PSO is combined with a two-criteria GA to locate the global optimal region of SOOCP. Thirdly, sequential quadratic programming (SQP) algorithm is applied in the global optimal region to achieve fast convergence to high local accuracy solutions satisfying optimality conditions. Finally, the effectiveness and superiority of the proposed method are ultimately validated through numerical simulations and comparative analyses against state-of-the-art algorithms. Specifically, the hybrid intelligent optimization algorithm shows a 7.65% improvement in hypervolume and a 46.60% reduction in spacing metric compared to the suboptimal algorithm.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"363 2","pages":"Article 108378"},"PeriodicalIF":4.2,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号