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Rethinking Propellant-Optimal Powered Descent guidance

JOURNAL OF guidance CONTROL AND DYNAMICS 2024年第10期47卷 2016-2028页

作者： Lu, Ping Davami, Christopher San Diego State Univ Dept Aerosp Engn San Diego CA 92182 USA

The propellant-optimal powered descent solution requires a bang-bang thrust magnitude profile that switches instantaneously between the upper and lower bounds of the engine thrust. Implementing a bang-bang thrust pattern for guidance can be challenging for the engine and may cause practical and operational difficulties. Motivated by the observation that excellent propellant performance does not appear to be predicated on necessarily having a bang-bang thrust structure, a new powered descent guidance method is proposed in this work. The foundation lies in a propellant-optimal problem in which the thrust magnitude is parameterized by a user-prescribed continuous function of time. In particular, constant and linear functions are considered. The solution determines the optimal thrust direction, time of flight, and design parameters defining the thrust function. An indirect-method-based guidance algorithm is developed to solve this problem rapidly and reliably. Substantial evidence is provided to demonstrate that the solutions proposed in this paper in general yield a propellant consumption very close to that of the optimal bang-bang solution. Given its comparable propellant performance, implementability, and robustness, this paper makes a strong case that the proposed propellant-optimal powered descent guidance approach is preferred to the long-standing bang-bang guidance approach.

关键词： guidance and navigational algorithms Powered Descent guidance Soft Landing guidance Spacecraft guidance Propellant-Optimized Autonomous Spacecraft Entry, Descent and Landing Optimal Control Problem

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Multirobot Navigation Using Partially Observable Markov Decision Processes with Belief-Based Rewards

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第8期20卷 437-446页

作者： Tzikas, Alexandros E. Knowles, Derek Gao, Grace X. Kochenderfer, Mykel J. Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA Stanford Univ Dept Mech Engn Stanford CA 94305 USA

For the safe deployment of multirobot systems, accurate position estimation is necessary. We present a multirobot navigation algorithm that accomplishes the dual objective of reaching designated goal positions and maintaining low position uncertainty. We pose the problem as a ?-partially observable Markov decision process (?POMDP) to capture position uncertainty through a belief-based reward. The multirobot system employs a centralized extended Kalman filter (EKF) for state estimation, whose output is used as the observation as well as the belief in the ?POMDP. Using the EKF output as the observation and belief allows for the integration of the EKF in the action planning module of the system. To solve the ?POMDP, we adapt the online information particle filter tree algorithm to be compatible with the EKF closed-form output being used as both the observation and belief. We present results for the proposed navigation algorithm that show emergent behaviors for a multirobot system, where the robots move to provide inter-ranging measurements that minimize position uncertainty for all robots in the system. We also demonstrate that the proposed method outperforms a state-of-the-art solver (partially observable Monte Carlo planning with observation widening) and a direct-to-goal planner in terms of minimizing position uncertainty.

关键词： Extended Kalman Filter guidance and navigational algorithms Multi-Robot Systems Markov Decision Process

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Uncertainties and Radius Tracking in Flight-Path Angle guidance for Aerogravity-Assist

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JOURNAL OF guidance CONTROL AND DYNAMICS 2023年第5期46卷 910-923页

作者： Mazzaracchio, Antonio Univ Roma La Sapienza I-00185 Rome Italy

This paper addresses the problem regarding the influence of random dispersions related to the environment and system on a flight-path angle guidance algorithm using lift modulation for aerogravity-assist maneuvers on hyperbolic trajectories. To compensate for the effects of dispersions and uncertainties, an original radius tracking guidance model is presented, with a formulation for both the temporal domain and the true anomaly domain. This algorithm allows a vehicle to successfully track a suboptimal trajectory planned before a mission. A case study involving a maneuver on a hyperbolic trajectory to Venus performed by a waverider, which is a spacecraft characterized by a high lift-to-drag ratio, is presented. An uncertainty and sensitivity analysis is used to evaluate the impacts of dispersions in the inputs on the main parameters and the performance characterizing the mission. The analysis provides an extensive range of cases that identify the density and thickness of the atmosphere as the most influential dispersed inputs. Adequately tuning the radius tracking guidance parameters enables the efficient attenuation and compensation of the perturbative effects of the uncertainties for obtaining trajectories that are close to the nominal trajectories, estimated before the mission in the absence of dispersions, in terms of the angular deflection and velocity magnitude variation. The presented model can be easily implemented for onboard guidance purposes.

关键词： Flight Path Angle guidance and navigational algorithms Hyperbolic Trajectories Mathematical Analysis Hypersonic Aircraft Pearson Correlation Coefficient Orbital Maneuvers

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Target-Based guidance Method for Trajectories with Multiple Flybys

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JOURNAL OF guidance CONTROL AND DYNAMICS 2023年第5期46卷 1008-1014页

作者： Giordano, Carmine Varga, Gabor Mackenzie, Ruaraidh Boutonnet, Arnaud Topputo, Francesco Polytech Univ Milan Dept Aerosp Sci & Technol I-20156 Milan Italy European Space Agcy HSO GFA D-64293 Darmstadt Germany

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Convex-Optimization-Based Model Predictive Control for Space Debris Removal Mission guidance

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JOURNAL OF guidance CONTROL AND DYNAMICS 2024年第9期47卷 1885-1904页

作者： Wijayatunga, Minduli Charithma Armellin, Roberto Holt, Harry Pirovano, Laura Bombardelli, Claudio Univ Auckland Auckland Space Inst Mech Engn Dept 34 Princes St Auckland 1010 New Zealand Tech Univ Madrid Sch Aeronaut & Space Engn Space Dynam Grp Madrid 28040 Spain

A convex-optimization-based model predictive control (MPC) algorithm for the guidance of active debris removal missions is proposed in this work. A high-accuracy reference for the convex optimization is obtained through a split-Edelbaum approach that takes the effects of J2, drag, and eclipses into account. When the spacecraft deviates significantly from the reference trajectory, a new reference is calculated through the same method to reach the target debris. When required, phasing is integrated into the transfer. During the mission, the phase of the spacecraft is adjusted to match that of the target debris at the end of the transfer by introducing intermediate waiting times. The robustness of the guidance scheme is tested in a high-fidelity dynamic model that includes thrust errors and misthrust events. The guidance algorithm performs well without requiring successive convex iterations. Monte Carlo simulations are conducted to analyze the impact of these thrust uncertainties on the guidance. Simulation results show that the proposed convex-MPC approach can ensure that the spacecraft can reach its target despite significant uncertainties and long-duration misthrust events.

关键词： Mathematical Optimization Active Debris Removal Model Predictive Control Monte Carlo Simulation guidance and navigational algorithms Orbital Elements Model predictive control Eclipses Duty cycle Electric Propulsion

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Ensemble Transfer Learning Midcourse guidance Algorithm for Velocity Maximization

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第4期20卷 204-215页

作者： Jin, Tianyu He, Shaoming Beijing Inst Technol Beijing 100081 Peoples R China Sch Aerosp Engn 5th Zhongguancun South St Beijing Peoples R China

This paper proposes an ensemble transfer learning guidance algorithm for angular-constrained midcourse guidance to maximize the terminal velocity. The algorithm developed improves the generalization capability of the trained deep neural network to adapt to a new environment. First several deep neural guidance networks are trained for some specific working environments via supervised learning. A small-scale ensemble transfer learning network is then leveraged to fuse the knowledge of different pretrained deep neural network. This requires much less labeled data to transfer existing knowledge to a new working environment and hence greatly improves the learning efficiency, compared to the supervised learning philosophy. Extensive numerical simulations are performed to demonstrate the effectiveness of the proposed algorithm.

关键词： Missile guidance and Control Generative Adversarial Network guidance and navigational algorithms Terminal Velocity Flight Path Angle Aerodynamic Coefficients Biased Proportional Navigation guidance Optimal Control Theory Aerodynamic Performance

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Online Corrections to Neural Policy guidance for Pinpoint Powered Descent

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JOURNAL OF guidance CONTROL AND DYNAMICS 2024年第5期47卷 945-963页

作者： Cho, Namhoon Shin, Hyo-Sang Tsourdos, Antonios Amato, Davide Cranfield Univ Ctr Autonomous & Cyber Phys Syst Sch Aerosp Transport & Mfg Cranfield MK43 0AL England Cranfield Univ Ctr Autonomous & Cyber Phys Syst Sch Aerosp Transport & Mfg Guidance Nav & Control Cranfield MK43 0AL England Imperial Coll London Fac Engn Dept Aeronaut Spacecraft Engn London SW7 2AZ England

This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables. The proposed approach is to linearize the dynamics around the baseline values of its arguments and then to solve for the corrective input required to transfer the perturbed trajectory to precisely known or desired values at specific time points, in other words, the interim points. Depending on the type of decision variables to adjust, parameter correction and control function correction methods are developed. These incremental correction methods can be used as a means to compensate for the prediction errors of pretrained neural networks in real-time applications where high accuracy of the prediction of dynamical systems at prescribed time points is imperative. In this regard, the online update approach can be useful for enhancing overall targeting accuracy of finite-horizon control subject to point constraints using a neural policy. A numerical example demonstrates the effectiveness of the proposed approach in an application to a powered descent problem on Mars.

关键词： Artificial Neural Network Finite Horizon Optimal Control guidance, Navigation, and Control Systems Representation Learning Entry, Descent, and Landing guidance and navigational algorithms Neural Ordinary Differential Equations Powered Descent guidance Incremental Correction Sensitivity

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Rocket Landing guidance Based on Linearization-Free Convexification

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JOURNAL OF guidance CONTROL AND DYNAMICS 2024年第2期47卷 217-232页

作者： Yang, Runqiu Liu, Xinfu Song, Zhengyu Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China China Acad Launch Vehicle Technol Beijing 100076 Peoples R China

The landing problem of a reusable rocket is generally a highly constrained and nonconvex optimization problem. In this paper, we will convexify the problem without relying on any linearization. Specifically, we propose to quickly determine some of the state variables in advance so that the nonlinearity of the dynamics is greatly reduced, and then we accurately convexify the problem by change of variables and transformation of the optimization objective. This convexification process enables us to design an iterative algorithm that can converge very reliably, and the convergence does not rely on any trust region constraint. It should be highlighted that the algorithm is very efficient, generally taking just milliseconds to converge on a personal computer. Furthermore, by ensuring the recursive feasibility of calling the iterative algorithm in each guidance cycle, we can design a landing guidance algorithm that is able to achieve precise landing under various uncertainties and disturbances. Numerical examples are provided to demonstrate the high performance of the iterative algorithm and the landing guidance algorithm.

关键词： Rockets guidance and navigational algorithms Flight Path Angle Aerodynamic Coefficients Soft Landing Optimization Algorithm Convex Optimization Optimal Control Problem Reusable Launch Vehicle Powered Descent guidance

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Hybrid Zero-Effort-Miss/Zero-Effort-Velocity guidance for Powered Descent Phase

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JOURNAL OF guidance CONTROL AND DYNAMICS 2024年第5期47卷 1026-1037页

作者： Gong, Youmin Guo, Yanning Lyu, Yueyong Ma, Guangfu Wang, Pengyu Harbin Inst Technol Shenzhen Dept Automat Shenzhen 518055 Peoples R China Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Harbin Inst Technol Dept Automat Harbin 150001 Peoples R China Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 34141 South Korea

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Large Field-of-View Infrared Horizon Sensor Attitude Correction for Earth's Oblateness

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JOURNAL OF guidance CONTROL AND DYNAMICS 2023年第10期46卷 2024-2032页

作者： Deng, Huajian Wang, Hao Liu, Yang Jin, Zhonghe Zhejiang Univ Microsatellite Res Ctr Sch Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China

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