We present Contingency Model Predictive Control (CMPC), a motion planning and control framework that optimizes performance objectives while simultaneously maintaining a contingency plan -- an alternate trajectory that avoids a potential hazard. By preserving the existence of a feasible avoidance trajectory, CMPC anticipates emergency and keeps the controlled system in a safe state that is selectively robust to the identified hazard. We accomplish this by adding an additional prediction horizon in parallel to the typical Model Predictive Control (MPC) horizon. This extra horizon is constrained to guarantee safety from the contingent threat and is coupled to the nominal horizon at its first command. Thus, the two horizons negotiate to compute commands that are both optimized for performance and robust to the contingent event. This article presents a linear formulation for CMPC, illustrates its key features on a toy problem, and then demonstrates its efficacy experimentally on a full-size automated road vehicle that encounters a realistic pop-out obstacle. Contingency MPC approaches potential emergencies with safe, intuitive, and interpretable behavior that balances conservatism with incentive for high performance operation.

中文翻译：

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线性时变系统的权变模型预测控制

我们介绍了应急模型预测控制（CMPC），这是一种运动计划和控制框架，可以优化性能目标并同时维护应急计划-避免潜在危险的替代轨迹。通过保留可行的回避轨迹的存在，CMPC可以预测紧急情况，并将受控系统保持在对选定的危害有选择性的鲁棒性的安全状态。我们通过与典型的模型预测控制（MPC）视域并行添加一个附加的预测视域来实现此目的。这种额外的视野被限制以保证安全免受偶然威胁的威胁，并且在其第一命令时被耦合到名义视野。因此，这两个视野协商计算出既针对性能进行了优化又对偶发事件具有鲁棒性的命令。本文介绍了CMPC的线性公式，阐明了其在玩具问题上的关键功能，然后在遇到现实弹出障碍物的全尺寸自动道路车辆上通过实验证明了其有效性。应急MPC通过安全，直观和可解释的行为来处理潜在的紧急情况，从而在保守性和高性能操作的动机之间取得平衡。