TransformerMPC：通过Transformer加速模型预测控制
TransformerMPC: Accelerating Model Predictive Control via Transformers
AI总结：
针对实时机器人应用中MPC计算负担重的问题，提出TransformerMPC方法，利用Transformer注意力机制实现在线约束移除与热启动，可适配任意MPC求解器，经仿真验证运行时间最高提升35倍且无性能损失。
摘要：
本文针对实时机器人应用中模型预测控制（Model Predictive Control，MPC）计算负担过重的问题展开研究。我们提出TransformerMPC方法，该方法利用Transformer中的注意力机制，同时实现在线约束移除与更优的热启动初始化，以此提升MPC算法的计算效率。具体而言，TransformerMPC仅选择有效约束纳入MPC问题，以此加速最优控制输入的计算，同时为优化过程提供热启动。该方法可确保最优解仍满足原始约束。TransformerMPC被设计为可与任意MPC求解器无缝集成，不受其实现方式的限制。为保证移除无效约束后约束仍被满足，我们执行了离线验证，确保MPC求解器生成的最优控制输入符合所有约束。通过在复杂机器人系统上开展大量数值仿真，TransformerMPC的有效性得到验证：其运行时间提升最高可达35倍，且无任何性能损失。
英文摘要：
In this paper, we address the problem of reducing the computational burden of Model Predictive Control (MPC) for real-time robotic applications. We propose TransformerMPC, a method that enhances the computational efficiency of MPC algorithms by leveraging the attention mechanism in transformers for both online constraint removal and better warm start initialization. Specifically, TransformerMPC accelerates the computation of optimal control inputs by selecting only the active constraints to be included in the MPC problem, while simultaneously providing a warm start to the optimization process. This approach ensures that the original constraints are satisfied at optimality. TransformerMPC is designed to be seamlessly integrated with any MPC solver, irrespective of its implementation. To guarantee constraint satisfaction after removing inactive constraints, we perform an offline verification to ensure that the optimal control inputs generated by the MPC solver meet all constraints. The effectiveness of TransformerMPC is demonstrated through extensive numerical simulations on complex robotic systems, achieving up to 35x improvement in runtime without any loss in performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
