LiDAR-Inertial-Based Absolute Positioning With Sparse DEM for Accurate Lunar Landing

Choe, Yeongkwon; Park, Chan Gook

doi:10.1109/taes.2024.3363678

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…However, this pursuit has been fraught with setbacks and challenges, as evidenced by the numerous failed attempts that have characterized recent endeavors in lunar exploration [5]. While significant research has been conducted on landing technologies such as propulsion systems, guidance algorithms, and landing mechanisms [6][7][8][9][10][11][12], there appears to be comparatively limited focus on modeling dynamics, stability, and control.…”

Section: Introductionmentioning

confidence: 99%

Guiding Lunar Landers: Harnessing Neural Networks for Dynamic Flight Control with Adaptive Inertia and Mass Characteristics

Ortega,

Enriquez-Fernandez,

Gonzalez

et al. 2024

Aerospace

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The autonomous control of landing procedures can provide the efficiency and precision that are vital for the successful, safe completion of space operations missions. Controlling a lander with this precision is challenging because the propellants, which will be expended during the operations, represent a significant fraction of the lander's mass. The mass variation of each tank profoundly influences the inertia and mass characteristics as thrust is generated and complicates the precise control of the lander state. This factor is a crucial consideration in our research and methodology. The dynamics model for our lander was developed where the mass, inertia, and center of mass (COM) vary with time. A feed-forward neural network (NN) is incorporated into the dynamics to capture the time-varying inertia tensor and COM. Moreover, the propellant takes time to travel through the feed lines from the storage tanks to the engine; also, the solenoid valves require time to open and close. Therefore, there are time delays between the actuator and the engine response. To take into account these sources of variations, a combined time delay is also included in the control loop to evaluate the effect of delays by fluid and mechanisms on the performance of the controller. The time delay is estimated numerically by a Computational Fluid Dynamics (CFD) model. As part of the lander's control mechanism, a thrust vector control (TVC) with two rotational gimbals and a reaction control system (RCS) are incorporated into the dynamics. Simple proportional, integral, and derivative (PID) controllers are designed to control the thrust, the gimbal angles of the TVC, and the torque required by the RCS to manipulate the lander's rotation and altitude. A complex mission with several numerical examples is presented to verify the hover and rotational motion control.

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Section: Introductionmentioning

confidence: 99%

Guiding Lunar Landers: Harnessing Neural Networks for Dynamic Flight Control with Adaptive Inertia and Mass Characteristics

Ortega,

Enriquez-Fernandez,

Gonzalez

et al. 2024

Aerospace

View full text Add to dashboard Cite

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Neural Network-Based Descent Control for Landers with Sloshing and Mass Variation: A Cascade and Adaptive PID Strategy

Ortega,

Shirin

2024

Aerospace

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Autonomous control of lunar landers is essential for successful space missions, where precision and efficiency are crucial. This study presents a novel control strategy that leverages proportional, integral, and derivative (PID) controllers to manage the altitude, attitude, and position of a lunar lander, considering time-varying mass and sloshing behavior. Additionally, neural network models are developed, to approximate the lander’s mass properties as they change during descent. The challenge lies in the significant mass variations due to fuel, oxidizer, and pressurant consumption, which affect the lander’s inertia and sloshing behavior and complicate control efforts. We have developed a control-oriented model incorporating these mass dynamics, employing multiple PID controllers to linearize the system and enhance control precision. Altitude is maintained by one PID controller, while two others adjust the thrust vector control (TVC) gimbal angles to manage pitch and roll, with a fourth controller governing yaw via a reaction control system (RCS). A cascade PD controller further manages position by feeding commands to the attitude controllers, ensuring the lander reaches its target location. The lander’s TVC mechanism, equipped with a spherical gimbal, provides thrust in the desired direction, with control angles α and β regulated by the PID controllers. To improve the model’s accuracy, we have introduced time delays caused by fluid dynamics and actuator response, modeled via computational fluid dynamics (CFD). Fluid sloshing effects are also simulated as external forces acting on the lander. The neural networks are trained using data derived from computer-aided design (CAD) simulations of the lander vehicle, specifically the inertia tensor and the center of mass (COM) based on the varying mass levels in the tanks. The trained neural networks (NNs) can then use lander tank levels and orientation to inform and accurately predict the lander’s COM and inertia tensor in real time during the mission. The implications of this research are significant for future lunar missions, offering enhanced safety and efficiency in vehicle descent and landing operations. Our approach allows for real-time estimation of the lander’s state and for precise execution of maneuvers, verified through complex numerical simulations of the descent, hover, and landing phases.

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LiDAR-Inertial-Based Absolute Positioning With Sparse DEM for Accurate Lunar Landing

Cited by 2 publications

References 47 publications

Guiding Lunar Landers: Harnessing Neural Networks for Dynamic Flight Control with Adaptive Inertia and Mass Characteristics

Guiding Lunar Landers: Harnessing Neural Networks for Dynamic Flight Control with Adaptive Inertia and Mass Characteristics

Neural Network-Based Descent Control for Landers with Sloshing and Mass Variation: A Cascade and Adaptive PID Strategy

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