用Transformer加速机器人控制中的模型预测控制，提升35倍效率。
TransformerMPC: Accelerating Model Predictive Control via Transformers
- 用Transformer注意力机制在线筛选关键约束，减少计算量。
- 通过离线验证确保去除无关约束后仍满足所有原始约束。
- 可无缝接入任意MPC求解器，适合实时机器人控制场景。
本文针对实时机器人应用中模型预测控制（MPC）的计算负担问题，提出TransformerMPC方法。该方法利用Transformer的注意力机制，在线选择活跃约束并提供更好的优化初始值，从而加速最优控制输入的计算。通过仅将活跃约束纳入MPC问题，同时为优化过程提供预热启动，确保在最优解处仍满足全部原始约束。该方法可无缝集成到任何MPC求解器中，不依赖具体实现。为保证移除非活跃约束后的约束满足性，采用离线验证机制确认优化结果满足所有约束。在复杂机器人系统上进行的大量数值仿真表明，TransformerMPC实现了最高达35倍的运行时间提升，且性能无损失。
原文摘要 · Abstract (English)
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.
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