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Deep-operator-network-based Mars entry parametric bank angle profile optimization
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DOI：10.1016/j.cja.2025.103578.png)
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Abstract
En 中文
Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry. This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions. The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization (PSO) algorithm. For illustrative purposes, a mission with the objective of maximizing terminal altitude is considered. The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods. The parameter optimization problem is addressed using the PSO algorithm, with reliability enhanced by increasing the PSO swarm size. To improve computational efficiency, an enhanced Deep Operator Network (DeepONet) is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly. Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver. Compared to the existing methodologies, this methodology offers a streamlined process, the reduced sensitivity to initial guesses, and the improved computational efficiency.
Keywords:
Bank angle profile
Mars entry
Neural networks
Operator learning
Particle swarm optimization
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