{"title":"参数化最优控制问题求解中的有限差分梯度与误差正交梯度","authors":"D. Kraft","doi":"10.1002/OCA.4660020207","DOIUrl":null,"url":null,"abstract":"Two non-linear programming algorithms based on the Lagrangian function are compared with respect to their computational efficiency for solving parameterized optimal control problems. If the most efficient, constrained variable metric method together with forward-difference gradients is used, the formulation and implementation of the adjoint variables can be avoided. This is especially convenient in the design phase of large complex systems.","PeriodicalId":54672,"journal":{"name":"Optimal Control Applications & Methods","volume":"2 1","pages":"191-199"},"PeriodicalIF":2.0000,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/OCA.4660020207","citationCount":"2","resultStr":"{\"title\":\"Finite-difference gradients versus error-quadrature gradients in the solution of parameterized optimal control problems\",\"authors\":\"D. Kraft\",\"doi\":\"10.1002/OCA.4660020207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Two non-linear programming algorithms based on the Lagrangian function are compared with respect to their computational efficiency for solving parameterized optimal control problems. If the most efficient, constrained variable metric method together with forward-difference gradients is used, the formulation and implementation of the adjoint variables can be avoided. This is especially convenient in the design phase of large complex systems.\",\"PeriodicalId\":54672,\"journal\":{\"name\":\"Optimal Control Applications & Methods\",\"volume\":\"2 1\",\"pages\":\"191-199\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2007-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1002/OCA.4660020207\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optimal Control Applications & Methods\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1002/OCA.4660020207\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optimal Control Applications & Methods","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/OCA.4660020207","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Finite-difference gradients versus error-quadrature gradients in the solution of parameterized optimal control problems
Two non-linear programming algorithms based on the Lagrangian function are compared with respect to their computational efficiency for solving parameterized optimal control problems. If the most efficient, constrained variable metric method together with forward-difference gradients is used, the formulation and implementation of the adjoint variables can be avoided. This is especially convenient in the design phase of large complex systems.
期刊介绍：
Optimal Control Applications & Methods provides a forum for papers on the full range of optimal and optimization based control theory and related control design methods. The aim is to encourage new developments in control theory and design methodologies that will lead to real advances in control applications. Papers are also encouraged on the development, comparison and testing of computational algorithms for solving optimal control and optimization problems. The scope also includes papers on optimal estimation and filtering methods which have control related applications. Finally, it will provide a focus for interesting optimal control design studies and report real applications experience covering problems in implementation and robustness.
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