This work presents DMPC (Data-and Model-Driven Predictive Control) to solve control problems in which some of the constraints or parts of the objective function are known, while others are entirely unknown to the controller. It is assumed that there is an exogenous “black box” system, e.g. a machine learning technique, that predicts the value of the unknown functions for a given trajectory. DMPC (1) provides an approach to merge both the model-based and black-box systems; (2) can cope with very little data and is sample efficient, building its solutions based on recently generated trajectories; and (3) improves its cost in each iteration until converging to an optimal trajectory, typically needing only a few trials even for nonlinear dynamics and objectives. Theoretical analysis of the algorithm is presented, proving that the quality of the trajectory does not worsen with each new iteration. We apply the DMPC algorithm to the motion planning of an autonomous vehicle with nonlinear dynamics.

这项工作提出了 DMPC（数据和模型驱动的预测控制）来解决控制问题，其中一些约束或目标函数的一部分是已知的，而其他一些则完全不为控制器所知。假定存在外生“黑匣子”系统，例如机器学习技术，其预测给定轨迹的未知函数的值。DMPC (1) 提供了一种合并基于模型和黑盒系统的方法；(2) 可以处理非常少的数据并且样本效率高，基于最近生成的轨迹构建其解决方案；(3) 在每次迭代中提高其成本，直到收敛到最佳轨迹，即使对于非线性动力学和目标，通常也只需要几次试验。提出了算法的理论分析，证明轨迹的质量不会随着每次新迭代而恶化。我们将 DMPC 算法应用于具有非线性动力学的自主车辆的运动规划。