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Self-Organizing Radial Basis Function Networks for Adaptive Flight Control

机译：自适应飞行控制的自组织径向基函数网络

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摘要

The performance of nonlinear flight-control algorithms, such as feedback linearization and dynamic inversion, is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the aircraft dynamics results in reduced performance and may lead to instability. A self-organizing parametrization structure is developed to augment the baseline dynamic inversion controller for a high-performance military aircraft. This algorithm is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. The controller is simulated for different situations, including control surface failures, modeling errors, and external disturbances. A performance measure of maximum tracking error is specified for the controllers a priori. Excellent tracking error minimization to a prespecified level using the adaptive component is achieved. The performance of the self-organizing radial-basis-function network-based controller is also compared with a fixed radial-basis-function network-based adaptive controller. While the fixed radial-basis-function network-based controller, which is tuned to compensate for control surface failures, fails to achieve the same performance under modeling uncertainty and disturbances, the self-organizing radial-basis-function network is able to achieve good tracking convergence under all specified error conditions.
机译：非线性飞行控制算法的性能，例如反馈线性化和动态反演，在很大程度上取决于动态模型的逼真度。对飞机动力学的不完全或不正确的了解会导致性能下降，并可能导致不稳定。开发了一种自组织参数化结构，以增强高性能军用飞机的基线动态反演控制器。该算法被证明是稳定的，可以保证任意跟踪误差性能。 Lyapunov理论推导了用于增长网络和调整参数的训练算法。除了扩展基本功能网络外，还采用了修剪策略以使网络规模尽可能小。针对不同情况对控制器进行了仿真，包括控制面故障，建模错误和外部干扰。先验地为控制器指定了最大跟踪误差的性能度量。使用自适应组件，可以将跟踪误差最小化到预定水平。自组织基于径向基函数网络的控制器的性能也与基于固定径向基函数网络的自适应控制器进行了比较。虽然基于径向基函数神经网络的固定控制器进行了调整以补偿控制面故障，但是在建模不确定性和扰动下仍无法达到相同的性能，而自组织径向基函数网络能够实现良好的性能。在所有指定的错误条件下跟踪收敛。

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  • 来源
    《Journal of guidance, control, and dynamics》 |2011年第3期|p.783-794|共12页
  • 作者

    Praveen Shankar; Rama K. Yedavaltf; John J. Burken;

  • 作者单位

    Arizona State University, Tempe, Arizona 85287,School for Engineering of Matter Transport and Energy;

    Ohio State University, Columbus, Ohio 43210,Department of Mechanical and Aerospace Engineering;

    NASA Dryden Flight Research Center, Edwards Air Force Base, California;

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