Design sensitivity analysis with polynomial chaos for robust optimization

Ren, Chengkun; Xiong, Fenfen; Mo, Bo; Chawdhury, Anik; Wang, Fenggang

doi:10.1007/s00158-020-02704-2

Cited by 9 publications

(1 citation statement)

References 35 publications

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“…Additionally, robust planning based on LAC is sensitive to initial conditions since it reshapes around a reference trajectory, often resulting in local optima that prematurely terminate the algorithm [17]. Given the limitations of LAC, scholars studying robust planning problems for Unmanned Aerial Vehicles (UAVs) have adopted methods based on nonlinear models such as Polynomial Chaos Expansion (PCE) [18][19][20][21][22] and Unscented Transform [23][24][25][26][27], enhancing the precision and efficiency of problem-solving.…”

Section: Introductionmentioning

confidence: 99%

Robust Trajectory Planning Of Gliding-Guided Projectiles With Weak Maneuverability

Yin,

Chen,

Wang

et al. 2024

Preprint

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Due to constraints in launch platform and cost, the maneuverability of gliding-guided projectiles is limited, necessitating a rational design of their trajectory schemes. To reduce the sensitivity of trajectory schemes to uncertainties while ensuring compatibility between flight schemes and guidance control systems, and fully exploiting the control capability of the projectile, a closed-loop robust trajectory planning method is proposed. Models of major uncertain factors and states deviation at the control-start point are established. Based on the NIPCE method, the stochastic dynamic model is transformed into a high-dimensional deterministic model with PCE coefficients as state variables, and the uncertainty propagation law is obtained. A PID algorithm is employed to design a tracking guidance law based on position error feedback, and open-loop and closed-loop robust trajectory planning models are established accordingly. The optimal control problem is solved by transforming it into a nonlinear programming problem using the direct shooting method. Simulation results indicate that the NIPCE method can significantly improve the computational efficiency of uncertainty propagation while ensuring accuracy. Open-loop robust planning can effectively mitigate the sensitivity of gliding trajectories to uncertainties but cannot completely eliminate terminal dispersion. Closed-loop robust planning effectively improves control effort consumption on the basis of open-loop planning.

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