Model predictive control (MPC) is a powerful tool for planning and controlling dynamical systems due to its capacity for handling constraints and taking advantage of preview information. Nevertheless, MPC performance is highly dependent on the choice of cost function tuning parameters. In this work, we demonstrate an approach for online automatic tuning of an MPC controller with an example application to an ecological cruise control system that saves fuel by using a preview of road grade. We solve the global fuel consumption minimization problem offline using dynamic programming and find the corresponding MPC cost function by solving the inverse optimization problem. A neural network fitted to these offline results is used to generate the desired MPC cost function weight during online operation. The effectiveness of the proposed approach is verified in simulation for different road geometries.

翻译：模型预测控制（MPC）因其能够处理约束并利用前视信息的优势，成为动力学系统规划与控制的强大工具。然而，MPC性能高度依赖于代价函数整定参数的选择。本研究提出一种在线自动整定MPC控制器的方案，并将其应用于通过预知道路坡度实现节油的生态巡航控制系统。我们利用动态规划离线求解全局油耗最小化问题，并通过逆向优化获得对应MPC代价函数。基于离线结果训练的神经网络用于在线生成目标MPC代价函数权重。针对不同道路几何工况的仿真验证了该方法的有效性。