The practical deployment of nonlinear model predictive control (NMPC) is often limited by online computation: solving a nonlinear program at high control rates can be expensive on embedded hardware, especially when models are complex or horizons are long. Learning-based NMPC approximations shift this computation offline but typically demand large expert datasets and costly training. We propose Sequential-AMPC, a sequential neural policy that generates MPC candidate control sequences by sharing parameters across the prediction horizon. For deployment, we wrap the policy in a safety-augmented online evaluation and fallback mechanism, yielding Safe Sequential-AMPC. Compared to a naive feedforward policy baseline across several benchmarks, Sequential-AMPC requires substantially fewer expert MPC rollouts and yields candidate sequences with higher feasibility rates and improved closed-loop safety. On high-dimensional systems, it also exhibits better learning dynamics and performance in fewer epochs while maintaining stable validation improvement where the feedforward baseline can stagnate.

翻译：非线性模型预测控制（NMPC）的实际部署常受限于在线计算：在嵌入式硬件上以高控制频率求解非线性程序可能成本高昂，尤其是当模型复杂或预测时域较长时。基于学习的NMPC近似方法虽将计算转移至离线阶段，但通常需要大量专家数据集及昂贵的训练成本。我们提出序列式AMPC（Sequential-AMPC）——一种通过跨预测时域共享参数生成MPC候选控制序列的顺序神经策略。为便于部署，我们为该策略设计了安全增强型在线评估与后备机制，形成安全序列式AMPC（Safe Sequential-AMPC）。与多个基准测试中的朴素前馈策略基线相比，Sequential-AMPC所需的专家MPC展开次数大幅减少，生成的候选序列具有更高的可行性率与改进的闭环安全性。在高维系统上，该方法还能在更少训练周期内展现出更优的学习动态与性能，同时保持稳定的验证改进效果，而前馈基线则可能停滞不前。