Amidst the surge in the use of Artificial Intelligence (AI) for control purposes, classical and model-based control methods maintain their popularity due to their transparency and deterministic nature. However, advanced controllers like Nonlinear Model Predictive Control (NMPC), despite proven capabilities, face adoption challenges due to their computational complexity and unpredictable closed-loop performance in complex validation systems. This paper introduces ExAMPC, a methodology bridging classical control and explainable AI by augmenting the NMPC with data-driven insights to improve the trustworthiness and reveal the optimization solution and closed-loop performance's sensitivities to physical variables and system parameters. By employing a low-order spline embedding, we reduce the open-loop trajectory dimensionality by over 95%, and integrate it with SHAP and Symbolic Regression from eXplainable AI (XAI) for an approximate NMPC, enabling intuitive physical insights into the NMPC's optimization routine. The prediction accuracy of the approximate NMPC is enhanced through physics-inspired continuous-time constraints penalties, reducing the predicted continuous trajectory violations by 93%. ExAMPC also enables accurate forecasting of the NMPC's computational requirements with explainable insights on worst-case scenarios. Experimental validation on automated valet parking and autonomous racing with lap-time optimization, demonstrates the methodology's practical effectiveness for potential real-world applications.

翻译：在人工智能（AI）技术广泛应用于控制领域的背景下，经典且基于模型的控制方法因其透明性和确定性特征而持续受到青睐。然而，诸如非线性模型预测控制（NMPC）等先进控制器，尽管已证明其性能优势，却因计算复杂度高以及在复杂验证系统中闭环性能的不可预测性而面临应用挑战。本文提出ExAMPC方法，通过将数据驱动的洞见融入NMPC，在经典控制与可解释AI之间架起桥梁，从而提升控制系统的可信度，并揭示优化解及闭环性能对物理变量和系统参数的敏感性。通过采用低阶样条嵌入技术，我们将开环轨迹维度降低超过95%，并将其与可解释AI（XAI）中的SHAP及符号回归方法相结合，构建出近似NMPC模型，从而实现对NMPC优化过程的直观物理解释。通过引入基于物理启发的连续时间约束惩罚项，近似NMPC的预测精度得到显著提升，预测连续轨迹违规率降低93%。ExAMPC还能准确预测NMPC的计算需求，并提供最坏情况下的可解释性分析。在自动代客泊车与具有单圈时间优化的自主竞速场景中的实验验证表明，该方法在实际应用中具有显著效能。