Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes
Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes
复制标题
DOI：
10.1177/1687814019859783
复制
发表时间：
2019-06
影响因子：
2.1
通讯作者：
Jaho Seo;K. Oh;Hongjun Noh
中科院分区：
文献类型：
--
作者：
Jaho Seo;K. Oh;Hongjun Noh
多轴全地形起重机惯性大，轴距长，动态响应慢于普通车辆。这对起重机的动力性能和转向性能有重要影响。因此，本研究的目的是为全地形起重机开发一种减少驾驶员转向努力的最优转向控制算法，并评估该算法的性能。为此，将模型预测控制技术应用于全地形起重机，提出了一种减少驾驶员转向工作量的全地形起重机转向控制算法。利用MATLAB/Simulink和ADAMS对现有转向系统和采用新算法的转向系统的转向性能进行了比较，并建立了人类驾驶员模型，对其进行了合理的性能评价。仿真包括双变道场景和道路转向模式下的弯曲路径场景。
All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.