论文标题
设置基于会员的非线性模型预测控制
Set Membership based Nonlinear Model Predictive Control
论文作者
论文摘要
我们提出了一种数值高效的非线性模型预测控制（NMPC）方法，称为基于SET成员的NMPC（SM-NMPC）。特别是，设定的成员资格方法用于从数据中得出最佳NMPC控制定律的近似值和紧密界限。这些数量用于减少NMPC优化问题的搜索域的维度和体积，从而显着缩短了计算时间。拟议的SM-NMPC策略在模拟中进行了测试，考虑到现实的自动驾驶汽车场景，例如平行停车场和行驶车道。
We present a numerically efficient Nonlinear Model Predictive Control (NMPC) approach, called Set Membership based NMPC (SM-NMPC). In particular, a Set Membership method is used to derive from data an approximation and tight bounds on the optimal NMPC control law. These quantities are used to reduce the dimensionality and volume of the search domain of the NMPC optimization problem, allowing a significant shortening of the computation time. The proposed SM-NMPC strategy is tested in simulation, considering realistic autonomous vehicle scenarios, like parallel parking and lane keeping maneuvers.
