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Sys."],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p> In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors\/uncertainties and external disturbances. However, MPC\u2019s sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics. <\/jats:p>","DOI":"10.1142\/s2301385024410206","type":"journal-article","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T05:16:56Z","timestamp":1705727816000},"page":"429-441","source":"Crossref","is-referenced-by-count":11,"title":["Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments"],"prefix":"10.1142","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9055-9276","authenticated-orcid":false,"given":"Zhaohan","family":"Feng","sequence":"first","affiliation":[{"name":"National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, P.\u00a0R.\u00a0China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2449-9793","authenticated-orcid":false,"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, P.\u00a0R.\u00a0China"},{"name":"Department of Control Science and Engineering, Tongji University, Shanghai 201804, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2623-2555","authenticated-orcid":false,"given":"Wei","family":"Xiao","sequence":"additional","affiliation":[{"name":"National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, P.\u00a0R.\u00a0China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9898-3129","authenticated-orcid":false,"given":"Jian","family":"Sun","sequence":"additional","affiliation":[{"name":"National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, P.\u00a0R.\u00a0China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9989-0418","authenticated-orcid":false,"given":"Bin","family":"Xin","sequence":"additional","affiliation":[{"name":"National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7266-2412","authenticated-orcid":false,"given":"Gang","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, P.\u00a0R.\u00a0China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"S2301385024410206BIB001","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abh1221"},{"key":"S2301385024410206BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2021.10.007"},{"key":"S2301385024410206BIB003","doi-asserted-by":"publisher","DOI":"10.1142\/S2301385024300014"},{"key":"S2301385024410206BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3230593"},{"key":"S2301385024410206BIB005","doi-asserted-by":"publisher","DOI":"10.1007\/s11370-018-00271-6"},{"key":"S2301385024410206BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2019.2942989"},{"key":"S2301385024410206BIB007","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-021-10011-y"},{"key":"S2301385024410206BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3322075"},{"key":"S2301385024410206BIB009","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.adg1462"},{"key":"S2301385024410206BIB010","volume-title":"Model Predictive Control: Theory and Design","author":"Rawlings J.","year":"2009"},{"key":"S2301385024410206BIB011","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593739"},{"key":"S2301385024410206BIB012","doi-asserted-by":"publisher","DOI":"10.1142\/13269"},{"key":"S2301385024410206BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2023.3244116"},{"key":"S2301385024410206BIB014","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2022.3209399"},{"key":"S2301385024410206BIB015","volume-title":"Reinforcement Learning: An Introduction","author":"Sutton R.","year":"2018","edition":"2"},{"key":"S2301385024410206BIB016","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2021.1003814"},{"key":"S2301385024410206BIB017","volume-title":"Proc. 37th Conf. 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