Model Predictive Control (MPC) provides an optimal control solution based on a cost function while allowing for the implementation of process constraints. As a model-based optimal control technique, the performance of MPC strongly depends on the model used where a trade-off between model computation time and prediction performance exists. One solution is the integration of MPC with a machine learning (ML) based process model which are quick to evaluate online. This work presents the experimental implementation of a deep neural network (DNN) based nonlinear MPC for Homogeneous Charge Compression Ignition (HCCI) combustion control. The DNN model consists of a Long Short-Term Memory (LSTM) network surrounded by fully connected layers which was trained using experimental engine data and showed acceptable prediction performance with under 5% error for all outputs. Using this model, the MPC is designed to track the Indicated Mean Effective Pressure (IMEP) and combustion phasing trajectories, while minimizing several parameters. Using the acados software package to enable the real-time implementation of the MPC on an ARM Cortex A72, the optimization calculations are completed within 1.4 ms. The external A72 processor is integrated with the prototyping engine controller using a UDP connection allowing for rapid experimental deployment of the NMPC. The IMEP trajectory following of the developed controller was excellent, with a root-mean-square error of 0.133 bar, in addition to observing process constraints.

翻译：模型预测控制（MPC）基于代价函数提供最优控制方案，同时允许实施过程约束。作为一种基于模型的最优控制技术，MPC的性能强烈依赖于所用模型，其中模型计算时间与预测性能之间存在权衡。一种解决方案是将MPC与基于机器学习（ML）的过程模型相结合，此类模型可在线快速评估。本研究展示了基于深度神经网络（DNN）的非线性MPC在均质压燃（HCCI）燃烧控制中的实验实现。该DNN模型由长短期记忆（LSTM）网络及环绕的全连接层构成，使用发动机实验数据训练，所有输出预测误差低于5%，表现出可接受的预测性能。利用该模型，MPC被设计用于跟踪指示平均有效压力（IMEP）和燃烧相位轨迹，同时最小化多个参数。借助acados软件包在ARM Cortex A72上实现MPC的实时运算，优化计算在1.4毫秒内完成。外部A72处理器通过UDP连接与原型发动机控制器集成，实现了非线性MPC的快速实验部署。所开发控制器对IMEP轨迹的跟踪性能优异，均方根误差为0.133巴，同时满足过程约束条件。