2015 IEEE Conference on Control Applications (CCA) 2015
DOI: 10.1109/cca.2015.7320730
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Fuel-optimal G-MPSP guidance for powered descent phase of soft lunar landing

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Cited by 13 publications
(4 citation statements)
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“…Note that the weight matrix is alternatively chosen to adjust the magnitude of the control history at different time steps. For the constrained optimal problem defined in equations (20) and (22), static optimization theory is applied, which gives the augmented cost function…”
Section: Angle Constrained Mpsp Guidancementioning
confidence: 99%
See 1 more Smart Citation
“…Note that the weight matrix is alternatively chosen to adjust the magnitude of the control history at different time steps. For the constrained optimal problem defined in equations (20) and (22), static optimization theory is applied, which gives the augmented cost function…”
Section: Angle Constrained Mpsp Guidancementioning
confidence: 99%
“…Afterwards, the state constrained MPSP formulation was tested on an air-to-air engagement scenario. Considering the soft landing problem of a lunar craft during the powered descent phase, Sachan and Padhi [20] proposed a fuel optimal guidance law under the help of G-MPSP, where both terminal position and velocity were satisfied by adjusting the magnitude and angle of thrust. Bringing in sliding mode control, S. Li and X. Li [21] solved the divergence problem of the common MPSP algorithm caused by model inaccuracy; better robustness was proved when applied to maneuvering target interception.…”
Section: Introductionmentioning
confidence: 99%
“…Since the thrust engine is usually fixed with the lunar lander's body, the vertical landing introduces a final attitude constraint, which can be represented by the final steering angle [8]. Consequently, there were some attempts to generate the optimal trajectory ending with a vertical landing [1], [2], [15]- [17]. For instance, in [1], the lunar landing trajectory was divided into two parts, with the final steering angle constraint augmented into the cost functional.…”
Section: Introductionmentioning
confidence: 99%
“…By integrating the equations of motion for altitude, velocity magnitude, and time analytically as a function of flight path angle, the thrust vector was used as a parameter to shape the trajectory. In [17], to handle the final steering angle constraint, a time-varying matrix was added to the cost functional, and the resulting optimal control problem was solved via model predictive static programming.…”
Section: Introductionmentioning
confidence: 99%