{"title":"Lateral Motion Control of a Maneuverable Aircraft Using Reinforcement Learning","authors":"Yu. V. Tiumentsev, R. A. Zarubin","doi":"10.3103/s1060992x2401003x","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Machine learning is currently one of the most actively developing research areas. Considerable attention in the ongoing research is paid to problems related to dynamical systems. One of the areas in which the application of machine learning technologies is being actively explored is aircraft of various types and purposes. This state of the art is due to the complexity and variety of tasks that are assigned to aircraft. The complicating factor in this case is incomplete and inaccurate knowledge of the properties of the object under study and the conditions in which it operates. In particular, a variety of abnormal situations may occur during flight, such as equipment failures and structural damage, which must be counteracted by reconfiguring the aircraft’s control system and controls. The aircraft control system must be able to operate effectively under these conditions by promptly changing the parameters and/or structure of the control laws used. Adaptive control methods allow to satisfy this requirement. One of the ways to synthesize control laws for dynamic systems, widely used nowadays, is LQR approach. A significant limitation of this approach is the lack of adaptability of the resulting control law, which prevents its use in conditions of incomplete and inaccurate knowledge of the properties of the control object and the environment in which it operates. To overcome this limitation, it was proposed to modify the standard variant of LQR (Linear Quadratic Regulator) based on approximate dynamic programming, a special case of which is the adaptive critic design (ACD) method. For the ACD-LQR combination, the problem of controlling the lateral motion of a maneuvering aircraft is solved. The results obtained demonstrate the promising potential of this approach to controlling the airplane motion under uncertainty conditions.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Memory and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s1060992x2401003x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Machine learning is currently one of the most actively developing research areas. Considerable attention in the ongoing research is paid to problems related to dynamical systems. One of the areas in which the application of machine learning technologies is being actively explored is aircraft of various types and purposes. This state of the art is due to the complexity and variety of tasks that are assigned to aircraft. The complicating factor in this case is incomplete and inaccurate knowledge of the properties of the object under study and the conditions in which it operates. In particular, a variety of abnormal situations may occur during flight, such as equipment failures and structural damage, which must be counteracted by reconfiguring the aircraft’s control system and controls. The aircraft control system must be able to operate effectively under these conditions by promptly changing the parameters and/or structure of the control laws used. Adaptive control methods allow to satisfy this requirement. One of the ways to synthesize control laws for dynamic systems, widely used nowadays, is LQR approach. A significant limitation of this approach is the lack of adaptability of the resulting control law, which prevents its use in conditions of incomplete and inaccurate knowledge of the properties of the control object and the environment in which it operates. To overcome this limitation, it was proposed to modify the standard variant of LQR (Linear Quadratic Regulator) based on approximate dynamic programming, a special case of which is the adaptive critic design (ACD) method. For the ACD-LQR combination, the problem of controlling the lateral motion of a maneuvering aircraft is solved. The results obtained demonstrate the promising potential of this approach to controlling the airplane motion under uncertainty conditions.
期刊介绍：
The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
