Online Weight-adaptive Nonlinear Model Predictive Control
Online Weight-adaptive Nonlinear Model Predictive Control
复制标题
在线权重自适应非线性模型预测控制
DOI：
10.1109/iros45743.2020.9341495
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
D. Scaramuzza
中科院分区：
文献类型：
--
作者：
Dimche Kostadinov;D. Scaramuzza
非线性模型预测控制（NMPC）是一种在约束条件下实现非线性动态过程控制的有效方法。在NMPC中，通常基于人类专家知识来选择相应的状态和控制成本的状态和控制权重，这通常反映了实际中可接受的稳定性。虽然被广泛使用，但这种方法对于执行具有最低位置误差和预测控制中的足够“平滑”变化的轨迹可能不是最佳的。此外，NMPC与在线权重更新策略的快速，敏捷，精确的无人机导航，还没有得到广泛的研究。为此，我们提出了一种新的控制问题的制定，允许在线更新的状态和控制权重。作为解决方案，我们提出了一种算法，包括两个交替的阶段：（i）状态和命令变量预测和（ii）权重更新。我们提出了一个数值评估与比较和分析不同的权衡问题的四旋翼导航。我们的计算机仿真结果表明，与具有固定权重的NMPC的标准解决方案相比，执行轨迹的精度提高了70%。
Nonlinear Model Predictive Control (NMPC) is a powerful and widely used technique for nonlinear dynamic process control under constraints. In NMPC, the state and control weights of the corresponding state and control costs are commonly selected based on human-expert knowledge, which usually reflects the acceptable stability in practice. Although broadly used, this approach might not be optimal for the execution of a trajectory with the lowest positional error and sufficiently "smooth" changes in the predicted controls. Furthermore, NMPC with an online weight update strategy for fast, agile, and precise unmanned aerial vehicle navigation, has not been studied extensively. To this end, we propose a novel control problem formulation that allows online updates of the state and control weights. As a solution, we present an algorithm that consists of two alternating stages: (i) state and command variable prediction and (ii) weights update. We present a numerical evaluation with a comparison and analysis of different trade-offs for the problem of quadrotor navigation. Our computer simulation results show improvements of up to 70% in the accuracy of the executed trajectory compared to the standard solution of NMPC with fixed weights.
DOI：
10.15607/rss.2019.xv.033
发表时间：
2019-02
期刊：
ArXiv
影响因子：
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作者：
Nolan Wagener;Ching-An Cheng;Jacob Sacks;Byron Boots
通讯作者：
Nolan Wagener;Ching-An Cheng;Jacob Sacks;Byron Boots