2016
DOI: 10.24200/sci.2016.3846
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First-Order Optimal Line-of-Sight Guidance for Stationary Targets

Abstract: Abstract. In this paper, a closed-loop optimal line-of-sight guidance law for rst-order control systems is derived for stationary targets. The problem is solved for the onedimensional case using normalized equations to obtain normalized guidance gains and performance curves. Three sets of normalized equations are introduced and discussed using di erent normalizing factors. The performances of the guidance laws are compared in normalized forms with zero-lag optimal guidance and a rst-order optimal scheme with s… Show more

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Cited by 5 publications
(4 citation statements)
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References 22 publications
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“…There are two types of path following algorithms namely geometric algorithms (Conte et al, 2004;Naini, 2015;Sujit et al, 2014;Nelson et al, 2007;Meenakshisundaram et al, 2010) The conventional Carrot Chasing Algorithm uses a fixed δ for every time step. However, the unique δ value in each time step must be determined according to the position and turning rate of USV to make accurate and stable navigation.…”
Section: Methodsmentioning
confidence: 99%
“…There are two types of path following algorithms namely geometric algorithms (Conte et al, 2004;Naini, 2015;Sujit et al, 2014;Nelson et al, 2007;Meenakshisundaram et al, 2010) The conventional Carrot Chasing Algorithm uses a fixed δ for every time step. However, the unique δ value in each time step must be determined according to the position and turning rate of USV to make accurate and stable navigation.…”
Section: Methodsmentioning
confidence: 99%
“…The proposed method discretizes the existing terrain pro le before conducting the search for optimal trajectories. In another research work, a closed-loop optimal guidance scheme for rstorder control systems was derived for a spin-stabilized ying vehicle [17]. Researchers also have used direct optimal control methods to solve trajectory-planning problems.…”
Section: Optimal Control Methodsmentioning
confidence: 99%
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mentioning
confidence: 99%
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mentioning
confidence: 99%