通过并行求解多阶段约束问题，提升机器人控制的安全性与性能。
Parallel-Constraint Model Predictive Control: Exploiting Parallel Computation for Improving Safety
- 并行求解多个时间步的MPC问题，动态调整安全集约束。
- 3关节机械臂仿真显示，4核计算即可显著提升安全性和性能。
- 适合对实时性与安全性要求高的机器人控制系统设计者。
确保约束满足是安全关键系统（如多数机器人平台）的核心需求，例如关节位置/速度/力矩限制和避障。受限系统通常采用模型预测控制（MPC），因其能自然处理约束，依赖数值优化。然而，对于非线性系统或约束，确保约束满足仍具挑战。一种提升安全性的经典方法是使用控制不变集（即安全集）。我们此前的工作表明，沿MPC时域让安全集约束逐渐退缩可改善安全性。本文进一步提出利用并行计算，同时求解多个不同时间步的安全集约束下的MPC问题，并根据用户定义标准选择最优解。在3关节机械臂上进行的大量仿真实验表明，即使仅使用4个计算核心，也能在安全性和性能方面实现显著提升。
原文摘要 · Abstract (English)
Ensuring constraint satisfaction is a key requirement for safety-critical systems, which include most robotic platforms. For example, constraints can be used for modeling joint position/velocity/torque limits and collision avoidance. Constrained systems are often controlled using Model Predictive Control, because of its ability to naturally handle constraints, relying on numerical optimization. However, ensuring constraint satisfaction is challenging for nonlinear systems/constraints. A well-known tool to make controllers safe is the so-called control-invariant set (a.k.a. safe set). In our previous work, we have shown that safety can be improved by letting the safe-set constraint recede along the MPC horizon. In this paper, we push that idea further by exploiting parallel computation to improve safety. We solve several MPC problems at the same time, where each problem instantiates the safe-set constraint at a different time step along the horizon. Finally, the controller can select the best solution according to some user-defined criteria. We validated this idea through extensive simulations with a 3-joint robotic arm, showing that significant improvements can be achieved in terms of safety and performance, even using as little as 4 computational cores.
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