A generalized algorithm for tuning UAS flight controllers*

Holly J. Wright, Reuben Strydom, M. Srinivasan
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引用次数: 1

Abstract

Proportional, Integral and Derivative (PID) controllers are among the most commonly used control systems throughout industry, and there is an increasing need to tune such controllers effectively and rapidly, especially in varying dynamic conditions. Here we present two versions of a generalized, iterative method for tuning PID controllers: Iterative Root Mean Square Optimization (iRMSE) and Iterative Weighted Root Mean Square Optimization (iWRMSE). The two methods are validated in Matlab and in a virtual environment, as well as in field tests with a quadcopter. The performance of our two methods are compared against five popular methods: Zeigler-Nichols, Cohen-Coon, Lambda, Root Mean Square Error (RMSE) and Integral Square Error (ISE). We find that iWRMSE optimization delivers performance that is better than that obtained using all of the other methods, including manual tuning. Both iRMSE and iWRMSE can be used on a wide range of systems. Due to their iterative nature, they are also likely to be more suitable for systems operating in noisy or variable environments.
一种用于调整无人机飞行控制器的广义算法*
比例,积分和导数(PID)控制器是整个工业中最常用的控制系统之一,并且越来越需要有效和快速地调整此类控制器,特别是在变化的动态条件下。在这里,我们提出了两个版本的广义迭代方法来整定PID控制器:迭代均方根优化(iRMSE)和迭代加权均方根优化(iWRMSE)。这两种方法在Matlab和虚拟环境中进行了验证,并在四轴飞行器上进行了现场测试。我们的两种方法的性能与五种流行的方法:Zeigler-Nichols, Cohen-Coon, Lambda,均方根误差(RMSE)和积分平方误差(ISE)进行了比较。我们发现iWRMSE优化提供的性能优于使用所有其他方法(包括手动调优)获得的性能。iRMSE和iWRMSE都可以在广泛的系统上使用。由于它们的迭代性质,它们也可能更适合在嘈杂或可变环境中运行的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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