Multi-constrained suboptimal powered descent guidance for lunar pinpoint soft landing

Zhang, Bo; Tang, Shuo; Pan, Binfeng

doi:10.1016/j.ast.2015.11.018

Cited by 40 publications

(16 citation statements)

References 23 publications

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“…where dry m denotes the dry mass of the lander. (8) during the entire flight. It is to indicate that the total system is under control with the thrust.…”

Section: Planetary Powered Descent Dynamics Model and Constraintsmentioning

confidence: 99%

“…Given the constraints defined in Eqs. (4) through (8), the powered descent trajectory planning can be formulated and relaxed as the following optimal control problem:…”

Section: B Trajectory Optimizationmentioning

confidence: 99%

“…The zero-effort-miss/zero-effort-velocity (ZEM/ZEV) algorithm usually focuses on minimizing the terminal state errors, which has excellent adaptability and robustness against retargeting maneuvers and uncertainties. However, the optimal fuel-consumption is also not taken into account in the ZEM/ZEV guidance [8]. The main concern of the optimal feedback guidance algorithms is always the guidance performance optimization under uncertain perturbations, but this kind of approaches usually doesn't consider retargeting [9].…”

Section: Introductionmentioning

confidence: 99%

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Computational guidance for planetary powered descent using collaborative optimization

Jiang

Tao³

2018

Aerospace Science and Technology

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An innovative computational guidance framework is proposed for planetary powered descent using collaborative optimization approach. First, the dynamical model and constraints for planetary powered descent are presented. Then, using collaborative optimization strategy, the computational guidance framework for powered descent is formulated as a multi-discipline optimization problem including trajectory optimization, optimal guidance, and system-level optimization. Finally, the computational guidance approach employs three algorithms for respectively solving the three optimization modules to implement numerical simulations. The optimality and robustness of the computational guidance approach are verified with all constraints satisfied even in the presence of initial state uncertainty.

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“…where dry m denotes the dry mass of the lander. (8) during the entire flight. It is to indicate that the total system is under control with the thrust.…”

Section: Planetary Powered Descent Dynamics Model and Constraintsmentioning

confidence: 99%

“…Given the constraints defined in Eqs. (4) through (8), the powered descent trajectory planning can be formulated and relaxed as the following optimal control problem:…”

Section: B Trajectory Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computational guidance for planetary powered descent using collaborative optimization

Jiang

Tao³

2018

Aerospace Science and Technology

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“…In their paper, without a glide slope constraint, the Mars lander was prevented from crashing onto the surface of Mars by adding an altitude constraint in the performance index. Zhang et al 14 proposed a model predictive static programming (MPSP) method to prevent a collision onto the surface of the Moon. The MPSP method was originally applied to the guidance systems of missiles and reusable launch vehicles, and it was found to be an effective method for solving optimal control problems with constraints.…”

Section: Introductionmentioning

confidence: 99%

Adaptive backstepping radial basis function neural network controller design for a Mars lander during the powered descent phase

Kang

Bang

Lee

2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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When a Mars lander is guided to follow a predetermined reference trajectory during the powered descent phase, large tracking errors occur due to strong perturbations caused by enormous external disturbances, such as the Martian atmosphere and wind and dust storms, as well as considerable uncertainties. The tracking performance is determined directly by the accuracy of the system model, especially with regard to nonlinear terms. In this paper, an adaptive backstepping radial basis function neural network controller is developed for a Mars lander to achieve precise tracking to a reference trajectory during the powered descent phase. The main part of the controller is designed with backstepping, and a radial basis function neural network with an online adaptive law for the weight vector is used as an auxiliary part to approximate unknown nonlinear functions, including the gravitational force, Coriolis force, centrifugal force, atmospheric drag force, atmospheric lift force, wind force, and unknown uncertainties. The proposed adaptive backstepping radial basis function neural network controller guarantees that tracking errors and radial basis sunction neural network weight estimation errors eventually converge to the uniformly ultimately bounded values according to the Lyapunov stability theory. Additionally, this study presents an online adaptive law for the weight vector. The simulation results show that the adaptive backstepping radial basis function neural network controller has an excellent tracking performance in the severe environmental conditions of Mars with strong external disturbances and large variations in uncertainties. Furthermore, this study reveals that the radial basis function neural network has an outstanding capability to approximate unknown nonlinear functions.

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“…Zhang et al 8 introduced the concept of pseudo control as time derivative of acceleration to attain the terminal state and control constraints simultaneously . In order to eliminate the unacceptable terminal error, the Model predictive Static Programming (MPSP) is proposed for generating the guidance command.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Explicit Guidance Algorithm for Terminal Descent Phase of Lunar Soft Landing

Banerjee

Padhi

2017

AIAA Guidance, Navigation, and Control Conference

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An explicit guidance algorithm for multi-constrained terminal descent phase of lunar soft landing is presented in this paper. A minimum jerk guidance is designed and extended for this purpose to achieve the terminal control and state constraints. The closed form jerk expression, obtained using the minimum jerk guidance is analyzed to obtain an explicit expression for acceleration command, which is the physical control variable for the guidance loop. The guidance formulation ensures the minimum rate of change of acceleration and vertical touchdown of the spacecraft towards a designated landing site with high terminal accuracy. The design features of the proposed guidance law are demonstrated using simulation results.

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Multi-constrained suboptimal powered descent guidance for lunar pinpoint soft landing

Cited by 40 publications

References 23 publications

Computational guidance for planetary powered descent using collaborative optimization

Computational guidance for planetary powered descent using collaborative optimization

Adaptive backstepping radial basis function neural network controller design for a Mars lander during the powered descent phase

An Optimal Explicit Guidance Algorithm for Terminal Descent Phase of Lunar Soft Landing

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