Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using p...
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Autor*in:	Chung-Cheng Chen [verfasserIn] Yen-Ting Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Almost disturbance decoupling COVID-19 epidemic prevention feedback linearized approach linear quadratic regulator particle swarm optimization quadrotor

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 26674-26695
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:26674-26695

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3057378

Katalog-ID:	DOAJ05276107X

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spelling	10.1109/ACCESS.2021.3057378 doi (DE-627)DOAJ05276107X (DE-599)DOAJ7ca1e0bc195a47e58d72a970f42b7430 DE-627 ger DE-627 rakwb eng TK1-9971 Chung-Cheng Chen verfasserin aut Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess “simultaneously” additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback. Almost disturbance decoupling COVID-19 epidemic prevention feedback linearized approach linear quadratic regulator particle swarm optimization quadrotor Electrical engineering. Electronics. Nuclear engineering Yen-Ting Chen verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 26674-26695 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:26674-26695 https://doi.org/10.1109/ACCESS.2021.3057378 kostenfrei https://doaj.org/article/7ca1e0bc195a47e58d72a970f42b7430 kostenfrei https://ieeexplore.ieee.org/document/9348898/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 26674-26695
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callnumber-first	T - Technology
author	Chung-Cheng Chen
spellingShingle	Chung-Cheng Chen misc TK1-9971 misc Almost disturbance decoupling misc COVID-19 epidemic prevention misc feedback linearized approach misc linear quadratic regulator misc particle swarm optimization misc quadrotor misc Electrical engineering. Electronics. Nuclear engineering Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances
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topic_title	TK1-9971 Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances Almost disturbance decoupling COVID-19 epidemic prevention feedback linearized approach linear quadratic regulator particle swarm optimization quadrotor
topic	misc TK1-9971 misc Almost disturbance decoupling misc COVID-19 epidemic prevention misc feedback linearized approach misc linear quadratic regulator misc particle swarm optimization misc quadrotor misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Almost disturbance decoupling misc COVID-19 epidemic prevention misc feedback linearized approach misc linear quadratic regulator misc particle swarm optimization misc quadrotor misc Electrical engineering. Electronics. Nuclear engineering
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title	Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances
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title_sort	feedback linearized optimal control design for quadrotor with multi-performances
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title_auth	Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances
abstract	Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess “simultaneously” additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback.
abstractGer	Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess “simultaneously” additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback.
abstract_unstemmed	Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess “simultaneously” additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback.
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title_short	Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances
url	https://doi.org/10.1109/ACCESS.2021.3057378 https://doaj.org/article/7ca1e0bc195a47e58d72a970f42b7430 https://ieeexplore.ieee.org/document/9348898/ https://doaj.org/toc/2169-3536
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