Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low-cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system.

中文翻译：

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基于深度学习的鲁棒 NMPC 控制器的概率性能验证

尽管最近在计算硬件、优化算法和定制实现方面取得了进展，但实时解决非线性模型预测控制问题仍然是一项重要挑战。当由于干扰、未知参数或测量和估计错误而存在不确定性时，这一挑战甚至更大。为了能够将高级控制方案应用于快速系统和低成本嵌入式硬件，我们建议使用深度学习来近似鲁棒非线性模型控制器，并使用概率验证技术验证其质量。我们提出了一种基于有限族的概率验证技术，结合广义最大值和约束退避的思想，使与一般性能指标相关的统计上有效的结论成为可能。