T2S-MPC让控制器实时感知时间变化，自适应调整策略应对动态扰动。
T2S-MPC: Time-Embedded Online Adaptive Model Predictive Control for Time-Varying Dynamics
- 用时间嵌入+双时标更新，让模型在线学习动态变化
- 在四旋翼上实现稳定控制与轨迹跟踪，优于传统方法
- 适合需要快速响应复杂扰动的机器人控制场景
基于学习的模型预测控制（MPC）利用神经网络进行在线模型学习，在系统动态偏离理想模型时表现良好。然而，现有方法主要针对特定或结构化的动态变化，难以处理更普遍、未知且不可预测的时间相关动态。为此，本文提出T2S-MPC框架，通过在线自适应学习残差动力学模型，并将其与基准模型融合于MPC框架中，实现快速演化在线规划。为使模型具备时间感知能力，我们采用结构化时间嵌入，并引入双时标更新机制，使控制器既能捕捉非平稳动态，又能在快速适应与稳定学习间取得平衡。我们在二维四旋翼上对多种时间变化扰动（包括线性漂移和周期性干扰）下的稳定与轨迹跟踪任务进行了评估。实验结果表明，T2S-MPC在控制性能上持续优于经典MPC、神经网络MPC及其消融变体，且在广泛扰动条件下表现出强鲁棒性，无需额外调参。代码已开源：https://github.com/Zeyuu0920/T2S_MPC
原文摘要 · Abstract (English)
Recent advances in learning-based model predictive control (MPC) have leveraged neural networks for online model learning, achieving strong performance when nonstationary system dynamics deviate from nominal models. However, existing approaches primarily address specific or relatively structured forms of dynamical variation, leaving more general, unknown, and unpredictable time-varying dynamics insufficiently handled. To tackle this challenge, we propose T2S-MPC, a framework that adaptively learns a residual dynamics model online and integrates it with the nominal model within the MPC framework to enable fast-evolving online planning. To make the model time-aware, we explicitly encode temporal information through a structured time embedding and employ a two-timescale update scheme, allowing the controller to capture nonstationary dynamics while balancing rapid adaptation with stable learning. We evaluate the proposed method on a 2D quadrotor across stabilization and trajectory tracking tasks under diverse time-varying disturbances, including linear drifting and periodic perturbations. Experimental results show that T2S-MPC consistently outperforms classical MPC, neural MPC, and ablated variants in control performance, while also demonstrating strong robustness across a wide range of disturbance conditions without additional tuning. The source code is publicly available at https://github.com/Zeyuu0920/T2S_MPC
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