{"title":"Optimizing trajectory tracking control for hypersonic flight vehicles via ADDHP.","authors":"Zhou Ziyang, Liang Xiaohui, Xu Bin","doi":"10.1016/j.isatra.2025.08.017","DOIUrl":null,"url":null,"abstract":"<p><p>This article proposes an intelligent hybrid control strategy for hypersonic flight vehicles (HFVs) that integrates sliding mode control (SMC) with actor-dependent dual heuristic programming (ADDHP) to address trajectory tracking challenges. An SMC baseline controller is first developed to ensure stable tracking with model uncertainties. Additionally, a novel angle of attack (AOA) protection mechanism is designed, which maintains the AOA within constraint boundaries by generating smooth modifying signals. Furthermore, multiple ADDHP-based optimal compensators are then implemented in the velocity and altitude subsystems. These model-free compensators dynamically optimize control performance through error-driven learning, significantly improving tracking accuracy and adaptability in complex environments. Lyapunov stability analysis proves the convergence of both SMC and ADDHP. The effectiveness and superiority of the proposed strategy are validated through comparative simulations.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5000,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.isatra.2025.08.017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This article proposes an intelligent hybrid control strategy for hypersonic flight vehicles (HFVs) that integrates sliding mode control (SMC) with actor-dependent dual heuristic programming (ADDHP) to address trajectory tracking challenges. An SMC baseline controller is first developed to ensure stable tracking with model uncertainties. Additionally, a novel angle of attack (AOA) protection mechanism is designed, which maintains the AOA within constraint boundaries by generating smooth modifying signals. Furthermore, multiple ADDHP-based optimal compensators are then implemented in the velocity and altitude subsystems. These model-free compensators dynamically optimize control performance through error-driven learning, significantly improving tracking accuracy and adaptability in complex environments. Lyapunov stability analysis proves the convergence of both SMC and ADDHP. The effectiveness and superiority of the proposed strategy are validated through comparative simulations.