发表机构
University of Minnesota–Twin Cities; DEVCOM Army Research Laboratory（明尼苏达大学双城分校; DEVCOM陆军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对部分状态测量下未知非线性系统的周期跟踪问题，采用可逆神经网络规避非凸逆问题，结合共形预测提供跟踪误差概率保证，在直流电机驱动机械负载上验证了方法有效性。
AI 中文摘要
针对部分状态测量下未知非线性系统的周期跟踪任务，本文研究数据驱动前馈控制的可证性问题。为此，采用可逆神经网络（INN）作为未知系统的替代模型，该选择可规避非凸逆问题的求解，消除相关逆误差，将跟踪误差可证性简化为替代建模问题。随后应用共形预测为替代建模误差提供有限样本概率保证，结合推导的跟踪误差界，得到前馈跟踪误差的边际可证性。最后，在带非线性摩擦的直流电机驱动机械负载上验证了该方法。
英文摘要
In this paper, we address the certification of datadriven feedforward control for periodic tracking of unknown nonlinear systems under partial state measurements. To this end, we adopt an invertible neural network (INN) as a surrogate for the unknown system. This choice allows us to bypass solving a nonconvex inversion problem, eliminating the associated inversion errors and reducing tracking error certification to a surrogate modeling problem. We then apply conformal prediction to provide finite-sample probabilistic guarantees on the surrogate modeling error which, through the derived tracking error bound, yield marginal certificates on feedforward tracking error. Finally, we demonstrate the approach on a DC-motor-driven mechanical load with nonlinear friction.
Comments6 pages, 6 figures
