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Data-Driven System Identification and Model Predictive Control of a Multirotor with an Unknown Suspended Payload

机译：具有未知暂停有效载荷的多轨电流的数据驱动系统识别和模型预测控制

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Transporting a suspended payload with a multirotor has many applications. Knowledge of the payload dynamics is required to adjust the control of the system to damp payload oscillations. Often, the dynamics of the payload are unknown and cannot be represented with an a priori model before a flight. This paper proposes a Model Predictive Control (MPC) architecture that controls a multirotor carrying an unknown suspended payload using a plant model from data-driven system identification techniques. Dynamic Mode Decomposition with Control (DMDc) and Hankel Alternative View Of Koopman with Control (HAVOKc) are the regression techniques used to identify system models without relying on modelling assumptions and by using only time series measurements. The standard Hankel Alternative View Of Koopman (HAVOK) is adapted slightly in this work for use with controlled systems. These two techniques are combined in the MPC architecture and compared against a conventional Proportional Integral Derivative (PID) system to control a multirotor with an unknown suspended payload within simulation. The results show that both MPC systems outperform the conventional system and achieve velocity control while simultaneously damping the payload swing angle. The proposed systems also show good adaptability with different payload parameters. Both system identification methods perform well with the presence of measurement noise.
机译：使用Multorot运输悬挂有效载荷具有许多应用。需要了解有效载荷动态，以调整系统的控制到潮湿的有效载荷振荡。通常，有效载荷的动态未知，不能在飞行前用先验模型表示。本文提出了一种模型预测控制（MPC）架构，其使用来自数据驱动系统识别技术的工厂模型来控制携带未知暂停有效载荷的多电路机。具有控制（DMDC）的动态模式分解和控制（HAVOKC）的Koopman的Hankel替代视图是用于识别系统模型的回归技术，而无需依赖于建模假设，并且仅使用时间序列测量值。 Koopman（HAVOK）的标准Hankel替代视图在这项工作中略微适用于控制系统。这两种技术在MPC架构中组合在MPC架构中，并与传统的比例积分导数（PID）系统进行比较，以在模拟中控制具有未知暂停有效载荷的多电路机。结果表明，两种MPC系统都优于传统系统，同时阻尼有效载荷摆动角度达到速度控制。所提出的系统还具有不同有效载荷参数的良好适应性。两个系统识别方法都在存在测量噪声的情况下进行良好。

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