Pub Date : 2026-01-14DOI: 10.1016/j.apm.2026.116762
Wengui Mao , Wei Feng , jie Wang , Kai Li , Houjing Li , Zhao Xiao
In view of the fact that the oil film pressure of sliding bearing is repeatedly propagated by time-varying uncertain information, resulting in irregular fluctuations, which is difficult to meet the accuracy requirements of the health assessment of sliding bearing, A time-varying uncertain oil film pressure identification method based on the measured axis trajectory with small sample size is proposed. Firstly, the interval process model is used to describe the time-varying uncertainty information to reduce the dependence on the sample size of the axis trajectory, and the orthogonal series expansion method of the interval process is used to decouple the time-varying uncertainty problem into the linear superposition of the median and radius of the oil film pressure. The traditional distributed dynamic load identification method is used to calculate the median oil film pressure. The oil film pressure radius is obtained according to the covariance matrix mapping relation between the the axis trajectory and the oil film pressure. The effectiveness and economy of the proposed method are demonstrated by numerical example and experimental application.
{"title":"Time-varying uncertain oil film pressure identification for journal bearing in wind turbine gearbox","authors":"Wengui Mao , Wei Feng , jie Wang , Kai Li , Houjing Li , Zhao Xiao","doi":"10.1016/j.apm.2026.116762","DOIUrl":"10.1016/j.apm.2026.116762","url":null,"abstract":"<div><div>In view of the fact that the oil film pressure of sliding bearing is repeatedly propagated by time-varying uncertain information, resulting in irregular fluctuations, which is difficult to meet the accuracy requirements of the health assessment of sliding bearing, A time-varying uncertain oil film pressure identification method based on the measured axis trajectory with small sample size is proposed. Firstly, the interval process model is used to describe the time-varying uncertainty information to reduce the dependence on the sample size of the axis trajectory, and the orthogonal series expansion method of the interval process is used to decouple the time-varying uncertainty problem into the linear superposition of the median and radius of the oil film pressure. The traditional distributed dynamic load identification method is used to calculate the median oil film pressure. The oil film pressure radius is obtained according to the covariance matrix mapping relation between the the axis trajectory and the oil film pressure. The effectiveness and economy of the proposed method are demonstrated by numerical example and experimental application.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"155 ","pages":"Article 116762"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.apm.2026.116753
Haojie Xu , Changwei Huang , Chengyi Xia
Fairness stands as a foundational principle in social and economic interactions, whereas in classical ultimatum game models it is generally grounded in linear, lossless payoffs that neglect the real-world costs of resource transfers. In this study, we extend the spatial ultimatum game by introducing a nonlinear loss function with two interpretable parameters: the loss intensity coefficient and the marginal loss sensitivity, which together capture diminishing marginal losses. Comprehensive numerical simulations reveal a pronounced optimization pattern: peak fairness appears when the loss intensity is large and the marginal loss sensitivity is moderate, a regime that simultaneously elevates average proposals and acceptance thresholds while damping long-term fluctuations. Mechanistically, the marginal loss effect imposes evolutionary pressure on unfair strategies, raising acceptance thresholds and compelling proposers to offer more equitable splits. Notably, the location of this high-fairness region remains robust across diverse network topologies, underscoring the generality of the marginal loss mechanism in fostering fairness. These findings contribute insights into the role of loss effects in promoting fairness, with potential applications in economic modeling, multi-agent systems, and the design of platforms that optimize social resource allocation.
{"title":"The evolution of fairness induced by marginal loss in ultimatum games within the networked population","authors":"Haojie Xu , Changwei Huang , Chengyi Xia","doi":"10.1016/j.apm.2026.116753","DOIUrl":"10.1016/j.apm.2026.116753","url":null,"abstract":"<div><div>Fairness stands as a foundational principle in social and economic interactions, whereas in classical ultimatum game models it is generally grounded in linear, lossless payoffs that neglect the real-world costs of resource transfers. In this study, we extend the spatial ultimatum game by introducing a nonlinear loss function with two interpretable parameters: the loss intensity coefficient and the marginal loss sensitivity, which together capture diminishing marginal losses. Comprehensive numerical simulations reveal a pronounced optimization pattern: peak fairness appears when the loss intensity is large and the marginal loss sensitivity is moderate, a regime that simultaneously elevates average proposals and acceptance thresholds while damping long-term fluctuations. Mechanistically, the marginal loss effect imposes evolutionary pressure on unfair strategies, raising acceptance thresholds and compelling proposers to offer more equitable splits. Notably, the location of this high-fairness region remains robust across diverse network topologies, underscoring the generality of the marginal loss mechanism in fostering fairness. These findings contribute insights into the role of loss effects in promoting fairness, with potential applications in economic modeling, multi-agent systems, and the design of platforms that optimize social resource allocation.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"155 ","pages":"Article 116753"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.apm.2026.116761
Quancheng Pu, Lu Yang, Tieshan Li
Achieving effective 3D path planning for multi-UAV in complex media poses a significant challenge for current methods. To address this challenge, this paper proposes a novel approach. First, a cylindrical coordinate system is adopted for node representation, reducing the spatial complexity of the optimization variables and enhancing the algorithm’s solvability. Second, the UAV path planning cost function is improved by decoupling costs from environmental parameters, thereby enhancing adaptability to various tasks. Drawing inspiration from the Levenberg-Marquardt algorithm, a new meta-heuristic optimization algorithm is developed, which integrates global search, local exploitation, and mutation strategies. This algorithm offers rapid optimization and robust global search capabilities, making it well-suited for complex problems such as multi-UAV path planning. To validate the method, comprehensive comparative experiments were conducted: (1) an analysis of node representations across three coordinate systems demonstrated that cylindrical coordinates reduce the search space, improve performance, and shorten computation time; (2) tests on CEC 2005 benchmark functions against advanced algorithms showed superior global optimization accuracy and convergence speed; and (3) simulations of single- and multi-UAV path planning in large-scale complex environments confirmed the method’s effectiveness and robustness. The proposed method holds promising potential for application in other practical optimization domains.
{"title":"A Levenberg-Marquardt-based optimization algorithm for multi-UAV path planning in complex media","authors":"Quancheng Pu, Lu Yang, Tieshan Li","doi":"10.1016/j.apm.2026.116761","DOIUrl":"10.1016/j.apm.2026.116761","url":null,"abstract":"<div><div>Achieving effective 3D path planning for multi-UAV in complex media poses a significant challenge for current methods. To address this challenge, this paper proposes a novel approach. First, a cylindrical coordinate system is adopted for node representation, reducing the spatial complexity of the optimization variables and enhancing the algorithm’s solvability. Second, the UAV path planning cost function is improved by decoupling costs from environmental parameters, thereby enhancing adaptability to various tasks. Drawing inspiration from the Levenberg-Marquardt algorithm, a new meta-heuristic optimization algorithm is developed, which integrates global search, local exploitation, and mutation strategies. This algorithm offers rapid optimization and robust global search capabilities, making it well-suited for complex problems such as multi-UAV path planning. To validate the method, comprehensive comparative experiments were conducted: (1) an analysis of node representations across three coordinate systems demonstrated that cylindrical coordinates reduce the search space, improve performance, and shorten computation time; (2) tests on CEC 2005 benchmark functions against advanced algorithms showed superior global optimization accuracy and convergence speed; and (3) simulations of single- and multi-UAV path planning in large-scale complex environments confirmed the method’s effectiveness and robustness. The proposed method holds promising potential for application in other practical optimization domains.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"156 ","pages":"Article 116761"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.apm.2026.116763
Chuang Yang, Jiangong Yu, Zhi Li, Xiaoming Zhang
As core theories of non-classical continuum theory, the nonlocal, nonlocal strain gradient theory, and Gurtin-Murdoch surface elasticity theories have yet to resolve how to determine the high-order modal scale parameters and applicability in wave propagation. In this study, molecular dynamics simulations combined with a GA-BP neural network were used to calibrate the scale parameters of higher-order Shear Horizontal (SH) modes and investigate the applicability of these theories. The results show the nonlocal and nonlocal strain gradient theories exhibit good applicability, whereas the Gurtin-Murdoch surface elasticity theory alone is inapplicable to higher-order modes. However, when combined with the nonlocal theory, it can accurately describe them. Moreover, the scale parameters exhibit clear differences between higher- and lower-order modes, with the scale parameters of higher-order modes being smaller than those of lower-order modes. Theoretical predictions based on the calibrated parameters agree well with MD simulations, with errors below 2%. These findings provide a basis for evaluating the applicability of non-classical continuum theories and guiding the design of sensitive nanodevices.
{"title":"Applicability of Shear Horizontal guided waves in non-classical continuum theory and calibration of higher-order mode scale parameters","authors":"Chuang Yang, Jiangong Yu, Zhi Li, Xiaoming Zhang","doi":"10.1016/j.apm.2026.116763","DOIUrl":"10.1016/j.apm.2026.116763","url":null,"abstract":"<div><div>As core theories of non-classical continuum theory, the nonlocal, nonlocal strain gradient theory, and Gurtin-Murdoch surface elasticity theories have yet to resolve how to determine the high-order modal scale parameters and applicability in wave propagation. In this study, molecular dynamics simulations combined with a GA-BP neural network were used to calibrate the scale parameters of higher-order Shear Horizontal (SH) modes and investigate the applicability of these theories. The results show the nonlocal and nonlocal strain gradient theories exhibit good applicability, whereas the Gurtin-Murdoch surface elasticity theory alone is inapplicable to higher-order modes. However, when combined with the nonlocal theory, it can accurately describe them. Moreover, the scale parameters exhibit clear differences between higher- and lower-order modes, with the scale parameters of higher-order modes being smaller than those of lower-order modes. Theoretical predictions based on the calibrated parameters agree well with MD simulations, with errors below 2%. These findings provide a basis for evaluating the applicability of non-classical continuum theories and guiding the design of sensitive nanodevices.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"155 ","pages":"Article 116763"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.apm.2026.116776
Xiaoyu Yang , Zhigeng Fang
To address the challenges of data fluctuations and sparse sampling in prognostic and health management, this paper proposes a hybrid Bayesian-calibrated grey model that enhances prediction robustness and accuracy by systematically integrating prior evolution knowledge from homogeneous historical samples into the grey modeling framework. The model incorporates a Bayesian calibration mechanism implemented through three key steps: first, constructing a prior distribution for the development coefficient based on historically similar samples; second, deriving a posterior estimate of the development coefficient via Bayesian inference to mitigate the impact of sampling data fluctuations; third, obtaining the prediction results from the general solution of the grey differential equation. The model’s performance is evaluated through numerical experiments and a practical task of predicting lubricant iron content in wind turbine gearboxes. Experimental results demonstrate that the proposed model exhibits excellent anti-interference capability, significantly improves prediction accuracy and robustness compared to conventional grey models, while also providing reliable interval forecasts. This framework offers a novel and robust solution for forecasting under data-sparse conditions, advancing the application of grey models in engineering prognostics.
{"title":"A hybrid bayesian calibrated grey model for robust lubricant wear debris forecasting","authors":"Xiaoyu Yang , Zhigeng Fang","doi":"10.1016/j.apm.2026.116776","DOIUrl":"10.1016/j.apm.2026.116776","url":null,"abstract":"<div><div>To address the challenges of data fluctuations and sparse sampling in prognostic and health management, this paper proposes a hybrid Bayesian-calibrated grey model that enhances prediction robustness and accuracy by systematically integrating prior evolution knowledge from homogeneous historical samples into the grey modeling framework. The model incorporates a Bayesian calibration mechanism implemented through three key steps: first, constructing a prior distribution for the development coefficient based on historically similar samples; second, deriving a posterior estimate of the development coefficient via Bayesian inference to mitigate the impact of sampling data fluctuations; third, obtaining the prediction results from the general solution of the grey differential equation. The model’s performance is evaluated through numerical experiments and a practical task of predicting lubricant iron content in wind turbine gearboxes. Experimental results demonstrate that the proposed model exhibits excellent anti-interference capability, significantly improves prediction accuracy and robustness compared to conventional grey models, while also providing reliable interval forecasts. This framework offers a novel and robust solution for forecasting under data-sparse conditions, advancing the application of grey models in engineering prognostics.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"156 ","pages":"Article 116776"},"PeriodicalIF":4.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1016/j.apm.2026.116754
Min Luo , Jiaxin Wu
Spatial-temporal prediction of turbulence remains an important and challenging task in fluid dynamics. This study proposes a fractal invariance-constrained deep learning model, which is characterized by two novel components: (1) a main network equipped with a multi-scale feature reuse mechanism for reduced-order modelling and flow state prediction; and (2) a physical constraint derived from the fractal theory to quantify and regularize scale-invariant self-similarities of fluid dynamic systems. The proposed physical constraint is then embedded into the main network, leading to the proposed model that integrates the efficiency of a deep learning network and the accuracy of physical constraints for flow state prediction. Moreover, a novel learning strategy is proposed to learn turbulence fluctuations at high frequencies and improve the training efficiency of the proposed model. Results on five self-affine fractal images and two turbulence cases demonstrate that the proposed model has achieved a threefold higher efficiency and 40 times improvement in prediction accuracy compared to the purely data-driven methods. Particularly in reconstructing physical quantities, such as the energy spectra and probability density functions of flow fields, the proposed model achieves up to a hundredfold improvement in accuracy. These results highlight the role of constraint in guiding the main network to accurately capture scale invariances and predict kinetic energy within high-frequency subranges.
{"title":"Fractal invariance-constrained deep learning for spatial-temporal prediction of turbulent flows","authors":"Min Luo , Jiaxin Wu","doi":"10.1016/j.apm.2026.116754","DOIUrl":"10.1016/j.apm.2026.116754","url":null,"abstract":"<div><div>Spatial-temporal prediction of turbulence remains an important and challenging task in fluid dynamics. This study proposes a fractal invariance-constrained deep learning model, which is characterized by two novel components: (1) a main network equipped with a multi-scale feature reuse mechanism for reduced-order modelling and flow state prediction; and (2) a physical constraint derived from the fractal theory to quantify and regularize scale-invariant self-similarities of fluid dynamic systems. The proposed physical constraint is then embedded into the main network, leading to the proposed model that integrates the efficiency of a deep learning network and the accuracy of physical constraints for flow state prediction. Moreover, a novel learning strategy is proposed to learn turbulence fluctuations at high frequencies and improve the training efficiency of the proposed model. Results on five self-affine fractal images and two turbulence cases demonstrate that the proposed model has achieved a threefold higher efficiency and 40 times improvement in prediction accuracy compared to the purely data-driven methods. Particularly in reconstructing physical quantities, such as the energy spectra and probability density functions of flow fields, the proposed model achieves up to a hundredfold improvement in accuracy. These results highlight the role of constraint in guiding the main network to accurately capture scale invariances and predict kinetic energy within high-frequency subranges.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"156 ","pages":"Article 116754"},"PeriodicalIF":4.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1016/j.apm.2026.116750
Agata Lonc, Barbara Domżała, Monika J. Piotrowska
In this paper, we propose a novel model that describes the movement of a group of n drones in an air corridor, where overtaking is allowed. The model combines main ideas from car-following traffic models with macroscopic concepts, such as the congestion of the air corridor. Furthermore, it incorporates the heterogeneity of drones through varying parameters, such as size, maximum velocity, and maximum acceleration. Apart from interactions between drones, the model allows for the wind compound to be taken into account. We prove the essential mathematical properties of the proposed model. Moreover, we derive the necessary conditions for all drones to move at the same constant speed and analyse the stability of such a situation. The process of overtaking is examined in two examples. In particular, we present all possible long-term scenarios for a special case of two drones. Then, we analyse the model with non-zero wind force, showing that wind strongly affects the dynamics of the whole system. Finally, we perform numerical simulations to illustrate the theoretical properties of the model.
{"title":"Autonomous drone model: A mathematical study","authors":"Agata Lonc, Barbara Domżała, Monika J. Piotrowska","doi":"10.1016/j.apm.2026.116750","DOIUrl":"10.1016/j.apm.2026.116750","url":null,"abstract":"<div><div>In this paper, we propose a novel model that describes the movement of a group of <em>n</em> drones in an air corridor, where overtaking is allowed. The model combines main ideas from car-following traffic models with macroscopic concepts, such as the congestion of the air corridor. Furthermore, it incorporates the heterogeneity of drones through varying parameters, such as size, maximum velocity, and maximum acceleration. Apart from interactions between drones, the model allows for the wind compound to be taken into account. We prove the essential mathematical properties of the proposed model. Moreover, we derive the necessary conditions for all drones to move at the same constant speed and analyse the stability of such a situation. The process of overtaking is examined in two examples. In particular, we present all possible long-term scenarios for a special case of two drones. Then, we analyse the model with non-zero wind force, showing that wind strongly affects the dynamics of the whole system. Finally, we perform numerical simulations to illustrate the theoretical properties of the model.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"155 ","pages":"Article 116750"},"PeriodicalIF":4.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-11DOI: 10.1016/j.apm.2026.116745
MohammadReza Ebrahimpour , Mihai Lungu , Saleh Mobayen , Rui Wang
Fixed-wing unmanned aerial vehicles are widely used in applications such as environmental monitoring, surveillance, and aerial mapping, where accurate attitude control is essential for flight stability and mission success. However, external disturbances, parametric uncertainties, and actuator dynamics with saturation and faults pose significant challenges to control performance and reliability. This paper proposes a robust and adaptive attitude-control strategy that addresses these challenges while maintaining low computational complexity. A fast finite-time second-order sliding-mode control method is designed by combining backstepping and sliding mode control techniques, providing enhanced robustness and effective suppression of chattering. The method is further augmented with an adaptive radial basis function neural network law and an auxiliary compensation system: the adaptive law updates only a single learning parameter, reducing computational load by approximately 53 % compared with conventional neural network–based adaptive methods, while the auxiliary system mitigates actuator saturation. Numerical simulations demonstrate that the proposed methods reduce tracking errors by up to 77 % relative to conventional approaches, maintain performance under severe disturbances and uncertainties, and achieve low computational overhead, highlighting their practical applicability in real-world unmanned aerial vehicle operations.
{"title":"Adaptive finite-time second-order sliding mode attitude control for fixed-wing UAVs","authors":"MohammadReza Ebrahimpour , Mihai Lungu , Saleh Mobayen , Rui Wang","doi":"10.1016/j.apm.2026.116745","DOIUrl":"10.1016/j.apm.2026.116745","url":null,"abstract":"<div><div>Fixed-wing unmanned aerial vehicles are widely used in applications such as environmental monitoring, surveillance, and aerial mapping, where accurate attitude control is essential for flight stability and mission success. However, external disturbances, parametric uncertainties, and actuator dynamics with saturation and faults pose significant challenges to control performance and reliability. This paper proposes a robust and adaptive attitude-control strategy that addresses these challenges while maintaining low computational complexity. A fast finite-time second-order sliding-mode control method is designed by combining backstepping and sliding mode control techniques, providing enhanced robustness and effective suppression of chattering. The method is further augmented with an adaptive radial basis function neural network law and an auxiliary compensation system: the adaptive law updates only a single learning parameter, reducing computational load by approximately 53 % compared with conventional neural network–based adaptive methods, while the auxiliary system mitigates actuator saturation. Numerical simulations demonstrate that the proposed methods reduce tracking errors by up to 77 % relative to conventional approaches, maintain performance under severe disturbances and uncertainties, and achieve low computational overhead, highlighting their practical applicability in real-world unmanned aerial vehicle operations.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"155 ","pages":"Article 116745"},"PeriodicalIF":4.4,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-11DOI: 10.1016/j.apm.2026.116741
Yixing Gong, Hao Wen
Manipulators mounted on flexible bases are widely used in space missions. However, their control is complicated by the dynamic coupling between the manipulator and the flexible base. The control objective of such a system is to simultaneously suppress the base’s vibrations and control the manipulator to move to its target configuration. Most existing approaches either use simplified mass-spring models that fail to capture complex dynamics or high-fidelity methods that are computationally expensive. This study focuses on manipulators mounted on large slender truss structures and presents a trajectory optimization scheme to accomplish the control task. To simplify analysis, the large slender truss is modeled as an Euler-Bernoulli beam with dynamics derived via the finite element method. For efficiency, the manipulator-truss dynamics is derived using recursive modeling. Subsequently, a trajectory optimization problem is formulated, in which the system’s implicit dynamics are employed as constraints. Afterwards, The trajectory optimization problem is subsequently transcribed into a nonlinear programming formulation and solved to generate optimal trajectories for feedback-based online tracking. Finally, numerical simulations using an Absolute Nodal Coordinate Formulation beam as reference demonstrate the effectiveness of the proposed trajectory optimization scheme, achieving the control tasks in both Absolute Nodal Coordinate Formulation- and Euler-Bernoulli-based models.
{"title":"Recursive modeling and trajectory optimization of space manipulator mounted on flexible structure","authors":"Yixing Gong, Hao Wen","doi":"10.1016/j.apm.2026.116741","DOIUrl":"10.1016/j.apm.2026.116741","url":null,"abstract":"<div><div>Manipulators mounted on flexible bases are widely used in space missions. However, their control is complicated by the dynamic coupling between the manipulator and the flexible base. The control objective of such a system is to simultaneously suppress the base’s vibrations and control the manipulator to move to its target configuration. Most existing approaches either use simplified mass-spring models that fail to capture complex dynamics or high-fidelity methods that are computationally expensive. This study focuses on manipulators mounted on large slender truss structures and presents a trajectory optimization scheme to accomplish the control task. To simplify analysis, the large slender truss is modeled as an Euler-Bernoulli beam with dynamics derived via the finite element method. For efficiency, the manipulator-truss dynamics is derived using recursive modeling. Subsequently, a trajectory optimization problem is formulated, in which the system’s implicit dynamics are employed as constraints. Afterwards, The trajectory optimization problem is subsequently transcribed into a nonlinear programming formulation and solved to generate optimal trajectories for feedback-based online tracking. Finally, numerical simulations using an Absolute Nodal Coordinate Formulation beam as reference demonstrate the effectiveness of the proposed trajectory optimization scheme, achieving the control tasks in both Absolute Nodal Coordinate Formulation- and Euler-Bernoulli-based models.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"155 ","pages":"Article 116741"},"PeriodicalIF":4.4,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-10DOI: 10.1016/j.apm.2026.116747
Jian-Yu Liu, Xiao-Yong Wen
This work employs the modified complex short pulse equation to model the nonlinear propagation of ultrashort optical pulses in fibers. Based on the hodograph transformation and the generalized Darboux transformation, four types of position-controlled cuspon localized wave solutions are constructed, comprising cuspon semi-rational soliton, cuspon rogue wave, cuspon periodic wave, and their cuspon hybrid interaction solutions, all of which are illustrated graphically. Unlike traditional single-valued smooth structures, we demonstrate single-valued, non-smooth cuspon-type localized wave structures with sharp peaks. Furthermore, by adjusting specific parameters, we can effectively control the spatial positions and shapes of these localized wave solutions. These results not only enrich the understanding of cuspon localized wave structures but also offer valuable tools for interpreting ultrashort optical pulse propagation under nonlinear conditions.
{"title":"Hodograph transformation and cuspon localized wave solutions for the modified complex short pulse equation","authors":"Jian-Yu Liu, Xiao-Yong Wen","doi":"10.1016/j.apm.2026.116747","DOIUrl":"10.1016/j.apm.2026.116747","url":null,"abstract":"<div><div>This work employs the modified complex short pulse equation to model the nonlinear propagation of ultrashort optical pulses in fibers. Based on the hodograph transformation and the generalized Darboux transformation, four types of position-controlled cuspon localized wave solutions are constructed, comprising cuspon semi-rational soliton, cuspon rogue wave, cuspon periodic wave, and their cuspon hybrid interaction solutions, all of which are illustrated graphically. Unlike traditional single-valued smooth structures, we demonstrate single-valued, non-smooth cuspon-type localized wave structures with sharp peaks. Furthermore, by adjusting specific parameters, we can effectively control the spatial positions and shapes of these localized wave solutions. These results not only enrich the understanding of cuspon localized wave structures but also offer valuable tools for interpreting ultrashort optical pulse propagation under nonlinear conditions.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"155 ","pages":"Article 116747"},"PeriodicalIF":4.4,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}