This paper presents a deep learning based model predictive control (MPC) algorithm for systems with unmatched and bounded state-action dependent uncertainties of unknown structure. We utilize a deep neural network (DNN) as an oracle in the underlying optimization problem of learning based MPC (LBMPC) to estimate unmatched uncertainties. Generally, non-parametric oracles such as DNN are considered difficult to employ with LBMPC due to the technical difficulties associated with estimation of their coefficients in real time. We employ a dual-timescale adaptation mechanism, where the weights of the last layer of the neural network are updated in real time while the inner layers are trained on a slower timescale using the training data collected online and selectively stored in a buffer. Our results are validated through a numerical experiment on the compression system model of jet engine. These results indicate that the proposed approach is implementable in real time and carries the theoretical guarantees of LBMPC.

翻译：本文针对存在未知结构且与状态-动作相关的有界非匹配不确定性的系统，提出一种基于深度学习的模型预测控制（MPC）算法。我们采用深度神经网络（DNN）作为学习型MPC（LBMPC）底层优化问题中的预测模块，用于估计非匹配不确定性。由于实时估计神经网络系数存在技术困难，通常认为DNN等非参数化预测模块难以与LBMPC结合使用。本文采用双时间尺度自适应机制：神经网络末层权重进行实时更新，而内层网络则以较慢时间尺度，利用在线采集并选择性存储在缓冲区中的训练数据进行训练。通过在喷气发动机压缩系统模型上的数值实验验证，结果表明所提方法具有实时可实现性，并保留了LBMPC的理论保障特性。