Adaptive powered descent guidance based on multi-phase pseudospectral convex optimization

Song, Yu; Miao, Xinyuan; Gong, Shengping

doi:10.1016/j.actaastro.2020.12.019

Cited by 41 publications

(8 citation statements)

References 27 publications

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“…To facilitate the trajectory calculation, the numerical values are normalized, and the interceptor's guidance motion model is processed dimensionless. The dimensionless target motion equation [36] is given by…”

Section: Problem Description 21 Model Establishmentmentioning

confidence: 99%

“…On the basis of the pseudospectral convex optimization technology framework, Sagliano et al [33][34][35] further propose the generalized hp pseudospectral convex programming method and the lobatto pseudospectral convex programming method, which improve the flexibility and efficiency of the algorithm optimization process. Song et al [36] propose an adaptive dynamic descent guidance method based on multi-stage pseudospectral convex optimization, which achieves adaptive trajectory planning during rocket landing. The above methods all perform convex transformation on the aircraft motion equation and constraints in the time domain, which achieves the optimization and generation of the aircraft trajectory.…”

Section: Introductionmentioning

confidence: 99%

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DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

Li,

et al. 2024

Aerospace

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To address the problem of low accuracy and efficiency in trajectory planning algorithms for interceptors facing multiple constraints during the midcourse guidance phase, an improved trajectory convex programming method based on the lateral distance domain is proposed. This algorithm can achieve fast trajectory planning, reduce the approximation error of the planned trajectory, and improve the accuracy of trajectory guidance. First, the concept of lateral distance domain is proposed, and the motion model of the midcourse guidance segment in the interceptor is converted from the time domain to the lateral distance domain. Second, the motion model and multiple constraints are convexly and discretely transformed, and the discrete trajectory convex model is established in the lateral distance domain. Third, the deep reinforcement learning algorithm is used to learn and train the initial solution of trajectory convex programming, and a high-quality initial solution trajectory is obtained. Finally, a dynamic adjustment method based on the distribution of approximate solution errors is designed to achieve efficient dynamic adjustment of grid points in iterative solving. The simulation experiments show that the improved trajectory convex programming algorithm proposed in this paper not only improves the accuracy and efficiency of the algorithm but also has good optimization performance.

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Section: Problem Description 21 Model Establishmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

Li,

et al. 2024

Aerospace

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show abstract

“…To address these methodological challenges, we propose a pseudospectral convex optimization formulation to balance the solution optimality and computational efficiency. The combination of pseudospectral discretization and convex optimization has recently been introduced to address trajectory optimization problems in aerospace engineering, [47][48][49][50] and shows great performance in solution accuracy, convergence rate, and computational efficiency.…”

Section: F I G U R E 1 Schematic Of Cav-based Optimal Speed Controlmentioning

confidence: 99%

Real‐time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Shi

Wang

LaClair

et al. 2023

Optim Control Appl Methods

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Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter‐vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop‐and‐go driving patterns. By applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real‐time on‐vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline.

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“…This method does not optimize the motion of target spacecraft directly, and the proposed objective function has a poor theoretical basis. Many studies have converted the optimal guidance law to a optimal control problem by using the Pontryagin's maximum principle [25,26]. Their main drawbacks are absence of important procedures to design the guidance law and scarcity of triaxial angular velocity detumbling analyzes.…”

Section: Introductionmentioning

confidence: 99%

Optimal detumbling guidance and control of plasma plume for tumbling spacecraft

Zhao,

Dai

2024

Nonlinear Dyn

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Detumbling is a crucial first step for on-orbit service and space debris removal. Recently, plume has become an efficient medium for detumbling failed spacecraft, which can avoid direct contact to improve operation safety. However, the traditional molecular plume may lead to an unaffordable fuel consumption, so it is necessary to propose a novel strategy to solve this problem. The Hall effect thruster is a common high specific impulse engine, which can generate high-speed plasma plume for contactless detumbling. The traditional plume models present difficulties for practical implementation due to the contradiction between the requirement of real-time computation and the limited computing capability of the spaceborne computer. To address the challenge of calculating the impact force, a fully analytical plasma plume model is proposed to substantially enhance the computing efficiency without loss of accuracy. A guidance law for optimal detumbling is proposed to mitigate the spin angular velocity while stabilizing nutation under multiple complex constraints. To facilitate the development of the detumbling control, a terminal non-singular sliding mode controller based on the saturation function (TSM-sat) is proposed, which can converge in finite time and avoid the chattering phenomenon. The Numerical results indicate that the proposed plasma plume model can significantly improve the computing efficiency and the optimal guidance law can stabilize the target within a reasonable time.

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Adaptive powered descent guidance based on multi-phase pseudospectral convex optimization

Cited by 41 publications

References 27 publications

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

Real‐time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Optimal detumbling guidance and control of plasma plume for tumbling spacecraft

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