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    Area of Science:

    • * Aerospace Engineering
    • * Control Theory
    • * Optimization Algorithms

    Background:

    • * Trajectory optimization problems with multiple objectives are complex due to nonlinear vehicle dynamics and mission requirements.
    • * Achieving a balance between satisfying constraints and optimizing objectives in these problems is a significant challenge.
    • * Existing methods struggle with discontinuity and nonlinearity, hindering effective trajectory planning.

    Purpose of the Study:

    • * To develop a novel evolutionary optimal control solver for multiobjective trajectory planning.
    • * To address the challenges posed by highly constrained and nonlinear trajectory optimization problems.
    • * To evaluate the efficacy of different constraint handling algorithms within an evolutionary framework.

    Main Methods:

    • * Application of a multiple-shooting discretization technique.
    • * Integration of the Novel Strength Genetic Algorithm III (NSGA-III) optimization algorithm.
    • * Incorporation and analysis of three distinct constraint handling algorithms.
    • * Comparison with established multiobjective optimization techniques.

    Main Results:

    • * The proposed evolutionary optimal control solver demonstrates superior convergence ability.
    • * Enhanced distribution of Pareto-optimal solutions compared to other evolutionary solvers.
    • * Successful handling of constraints and optimization of multiple objectives in complex scenarios.
    • * Validation through experimental studies and comparative analysis.

    Conclusions:

    • * The developed evolutionary optimal control solver offers a robust alternative for multiobjective trajectory optimization.
    • * The approach effectively addresses challenges associated with nonlinearity and discontinuity.
    • * It provides improved performance in terms of convergence and solution quality for continuous-time trajectory planning.