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首页> 外文期刊>Journal of guidance, control, and dynamics >Theory and Flight-Test Validation of a Concurrent-Learning Adaptive Controller
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Theory and Flight-Test Validation of a Concurrent-Learning Adaptive Controller

机译：并行学习自适应控制器的理论与试飞验证

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摘要

Theory and results of flight-test validation are presented for a novel adaptive law that concurrently uses current as well as recorded data for improving the performance of model reference adaptive control architectures. This novel adaptive law is termed concurrent learning. This adaptive law restricts the weight updates based on stored data to the null-space of the weight updates based on current data for ensuring that learning on stored data does not affect responsiveness to current data. This adaptive law alleviates the rank-1 condition on weight updates in adaptive control, thereby improving weight convergence properties and improving tracking performance. Lyapunov-like analysis is used to show that the new adaptive law guarantees uniform ultimate boundedness of all system signals in the framework of model reference adaptive control. Flight-test results confirm expected improvements in performance.
机译：提出了一种新颖的自适应定律的飞行测试验证的理论和结果，该定律同时使用当前数据和记录数据来改善模型参考自适应控制体系结构的性能。这种新颖的自适应定律称为并发学习。该自适应法则将基于存储的数据的权重更新限制为基于当前数据的权重更新的空空间，以确保对存储数据的学习不会影响对当前数据的响应。该自适应法则减轻了自适应控制中权重更新的等级-1条件，从而改善了权重收敛特性并提高了跟踪性能。类似于Lyapunov的分析表明，新的自适应律在模型参考自适应控制的框架内保证了所有系统信号的统一最终有界性。飞行测试结果证实了预期的性能改进。

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