Physics-informed disturbance estimation and nonlinear controller design for a multi-axis gimbal system

buir.contributor.authorLeblebicioglu, Damla
buir.contributor.authorAtesoglu, Ozgur
buir.contributor.authorCakmakci, Melih
buir.contributor.orcidLeblebicioglu, Damla|0000-0002-6288-2980
buir.contributor.orcidCakmakci, Melih|0000-0003-0686-9631
dc.citation.epage535en_US
dc.citation.issueNumber37en_US
dc.citation.spage530en_US
dc.citation.volumeNumber55en_US
dc.contributor.authorLeblebicioglu, Damla
dc.contributor.authorAtesoglu, Ozgur
dc.contributor.authorCakmakci, Melih
dc.date.accessioned2023-02-23T11:47:18Z
dc.date.available2023-02-23T11:47:18Z
dc.date.issued2022-11-22
dc.departmentDepartment of Mechanical Engineeringen_US
dc.description.abstractThe increasing demand for target tracking, environmental surveys, surveillance and mapping requires multi-axis gimbal systems with high tracking and stabilization performance. In this study, a new torque estimation structure is proposed to compute the complex disturbances negatively affecting the performance of the system. Two different control strategies based on Active Disturbance Rejection Control (ADRC) and Estimated Torque Model are implemented on a two-axis gimbal system. In the first strategy, the purpose is to improve the robustness, environmental adaptability and tracking accuracy of the system. The tuning effort of the ADRC is reduced by integrating a Neural Network (NN) based disturbance compensator (NN assisted ADRC). In the second strategy, NN is replaced with an Estimated Torque Model (ETM assisted ADRC), whose inputs come from plant outputs. The simulation results show that, both NN and ETM assisted control structures decrease the tracking errors. However, the improvements achieved by the physics-informed neural network-based estimator are more significant.en_US
dc.identifier.doi10.1016/j.ifacol.2022.11.237en_US
dc.identifier.eissn2405-8963
dc.identifier.urihttp://hdl.handle.net/11693/111630
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://dx.doi.org/10.1016/j.ifacol.2022.11.237en_US
dc.source.titleIFAC-PapersOnLineen_US
dc.subjectMissile guidance and controlen_US
dc.subjectModelingen_US
dc.subjectPhysics-informed neural network applicationsen_US
dc.titlePhysics-informed disturbance estimation and nonlinear controller design for a multi-axis gimbal systemen_US
dc.typeArticleen_US
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