论文标题
通过随机驱动器模型进行自适应巡航控制的基于学习的规避风险模型的预测控制
Learning-Based Risk-Averse Model Predictive Control for Adaptive Cruise Control with Stochastic Driver Models
论文作者
论文摘要
我们提出了一种基于学习的分配强大的模型预测控制方法，以设计自适应巡航控制系统（ACC）系统。我们使用具有连续动力学和离散的Markovian输入的混合模型将前面的车辆建模为自主随机系统。我们使用观察到的模式过渡估算了该模型的（未知）过渡概率，并同时确定围绕这些估计值的概率向量（歧义集）集，这些估计值包含具有很高置信度的真实过渡概率。然后，我们解决了一个规避风险的最佳控制问题，该问题假设这些集合中最差的分布。我们此外，我们还会得出一个可靠的终端约束集，并使用它来建立所得MPC方案的递归可行性。我们通过闭环模拟来验证理论结果并证明了该方案的理想特性。
We propose a learning-based, distributionally robust model predictive control approach towards the design of adaptive cruise control (ACC) systems. We model the preceding vehicle as an autonomous stochastic system, using a hybrid model with continuous dynamics and discrete, Markovian inputs. We estimate the (unknown) transition probabilities of this model empirically using observed mode transitions and simultaneously determine sets of probability vectors (ambiguity sets) around these estimates, that contain the true transition probabilities with high confidence. We then solve a risk-averse optimal control problem that assumes the worst-case distributions in these sets. We furthermore derive a robust terminal constraint set and use it to establish recursive feasibility of the resulting MPC scheme. We validate the theoretical results and demonstrate desirable properties of the scheme through closed-loop simulations.
