仅用输入输出数据就能设计最优控制器，无需系统模型。
A Data-Driven Algorithm for Model-Free Control Synthesis
- 基于输入输出数据和约束优化，直接估计LQR控制增益。
- 实测验证：在未知动力学的真机飞机上成功飞行测试。
- 适合无模型控制、航空航天等需高可靠性的场景。
本文提出一种算法，用于合成连续时间系统的最优无限时域LQR反馈控制器。该算法无需系统动力学信息，仅需有限长度的输入输出数据。文中给出了最优LQR增益与任意系统轨迹之间的一个必要条件，并基于此条件构建了基于约束优化的算法，以估计LQR增益矩阵。除标准反馈增益外，还可求得前馈增益，实现参考轨迹跟踪控制。本文提供了该方法的理论依据，并通过多个示例进行验证，包括在真实尺寸飞机上的验证飞行，其动力学特性未知。
原文摘要 · Abstract (English)
Presented is an algorithm to synthesize the optimal infinite-horizon LQR feedback controller for continuous-time systems. The algorithm does not require knowledge of the system dynamics but instead uses only a finite-length sampling of input-output data. A necessary condition that relates the optimal LQR gain to any arbitrary solution trajectory of the system is presented. An algorithm using this necessary condition, based on constrained optimization, is developed that estimates the LQR gain matrix. In addition to calculating the standard feedback gain matrix, a feedforward gain can be found to implement a reference tracking controller. This paper presents a theoretical justification for the method and shows several examples, including a validation test flight on a real scale aircraft with unknown dynamics.
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