Convex Optimization-based Entry Guidance for Spaceplane

Bae, Juho; Lee, Sang−Don; Kim, Young‐Won; Lee, Chang-Hun; Kim, Sung-Yug

doi:10.1007/s12555-021-0580-4

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…In order to verify the effectiveness of the rapid initial solution trajectory generation method based on the DDPG algorithm proposed, this paper evaluates the convergence of the DDPG algorithm training by observing the changes in average rewards; the specific meaning of average rewards can be found in Ref [30], and the average reward index is set to 800. Simultaneous selection of multiple terminal end positions ([ Figure 4a shows that the average reward of the DDPG algorithm shows an overall upward trend during the training process, and when the number of training iterations is 15,130, the average reward reaches the set training index, indicating that the DDPG algorithm can converge in the training of the initial solution generation task of the guidance trajectory.…”

Section: The Effectiveness Of Initial Solution Trajectories' Rapid Ge...mentioning

confidence: 99%

“…In Ref [30], for the aircraft re-entry guidance problem, the continuous linearization and convexification techniques are used to transform the problem into a continuous convex programming problem, and a convex optimization re-entry guidance method is designed to solve the problem, which reduces the sensitivity of the initial guess accuracy. Zhou et al [31] improve the efficiency and accuracy of the algorithm by designing a dynamic adjustment grid point method based on the original sequence convex programming algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…hierarchical structure; the critical network adopts the 11-300-2 hierarchical structure; the network parameter optimizer uses the Adam optimizer; and the network learning rate is set to 0.0001.6.2. The Effectiveness of Initial Solution Trajectories' Rapid Generation Method VerificationIn order to verify the effectiveness of the rapid initial solution trajectory generation method based on the DDPG algorithm proposed, this paper evaluates the convergence of the DDPG algorithm training by observing the changes in average rewards; the specific meaning of average rewards can be found in Ref[30], and the average reward index is set to 800. Simultaneous selection of multiple terminal end positions ([1 f h , 1 f z , 1 f x ] = [3 ×10 4 /re, 0, 3.38 × 10 5 /re], [ 2 f h , 2 f z , 2 f x ] = [3 × 10 4 /re, 2.5 × 10 4 /re, 3.38 × 10 5 /re], [ 3 f h , 3 f…”

mentioning

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: The Effectiveness Of Initial Solution Trajectories' Rapid Ge...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

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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“…At present, the research of trajectory online planning mainly faces the following difficulties: (1) The real-time performance of the online planning method is difficult to guarantee; (2) In order to ensure the efficiency of online planning, it is difficult to generate the real optimal trajectory with high accuracy; (3) Dynamic pressure, heat flow density, overload and other constraints restrict the efficiency of trajectory planning, which brings great difficulties to engineering implementation. In order to solve the difficulties encountered in online trajectory planning, a large number of researchers have conducted in-deep research, References [2][3][4][5][6] introduced convex optimization theory into the online reentry trajectory planning of aircrafts, making it easy to solve the trajectory and providing a new idea for online trajectory planning. However, this method requires a specific simulated flight trajectory as the initial value, which leads to strong restrictions.…”

Section: Introductionmentioning

confidence: 99%

Online Trajectory Planning Method for Midcourse Guidance Phase Based on Deep Reinforcement Learning

Shao

et al. 2023

Aerospace

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Concerned with the problem of interceptor midcourse guidance trajectory online planning satisfying multiple constraints, an online midcourse guidance trajectory planning method based on deep reinforcement learning (DRL) is proposed. The Markov decision process (MDP) corresponding to the background of a trajectory planning problem is designed, and the key reward function is composed of the final reward and the negative step feedback reward, which lays the foundation for the interceptor training trajectory planning method in the interactive data of a simulation environment; at the same time, concerned with the problems of unstable learning and training efficiency, a trajectory planning training strategy combined with course learning (CL) and deep deterministic policy gradient (DDPG) is proposed to realize the progressive progression of trajectory planning learning and training from satisfying simple objectives to complex objectives, and improve the convergence of the algorithm. The simulation results show that our method can not only generate the optimal trajectory with good results, but its trajectory generation speed is also more than 10 times faster than the hp pseudo spectral convex method (PSC), and can also resist the error influence mainly caused by random wind interference, which has certain application value and good research prospects.

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Fixed Range Horizon MPPI-based Missile Computational Guidance for Constrained Impact Angle

Kim

Lee

2023

Int. J. Control Autom. Syst.

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Convex Optimization-based Entry Guidance for Spaceplane

Cited by 10 publications

References 33 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

Online Trajectory Planning Method for Midcourse Guidance Phase Based on Deep Reinforcement Learning

Fixed Range Horizon MPPI-based Missile Computational Guidance for Constrained Impact Angle

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