提出高效采样方法，让高斯过程模型更安全可靠地用于实时控制。
Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework
- 在序列二次规划框架内迭代生成一致的动力学样本
- 可达集估计更精准，约束满足概率高
- 适合需要安全保证的实时控制系统设计
使用高斯过程回归学习不确定动态模型已被证明可实现高性能且具备安全性保障的控制策略，适用于复杂现实应用场景。然而，为保证计算可处理性，现有基于高斯过程的模型预测控制（GP-MPC）方法大多依赖对可达集的近似，这些近似要么过于保守，要么损害控制器的安全性保证。为此，本文提出一种鲁棒的GP-MPC公式，可高概率保证约束满足。为实现可计算性，我们提出一种基于采样的GP-MPC方法，该方法在序列二次规划框架内迭代生成来自高斯过程的一致动力学样本。通过两个数值案例验证了所提方法在可达集近似精度上的提升以及实时可行性计算时间的优势。
原文摘要 · Abstract (English)
Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-world applications. Yet, for computational tractability, most approaches for Gaussian process-based model predictive control (GP-MPC) are based on approximations of the reachable set that are either overly conservative or impede the controller's safety guarantees. To address these challenges, we propose a robust GP-MPC formulation that guarantees constraint satisfaction with high probability. For its tractable implementation, we propose a sampling-based GP-MPC approach that iteratively generates consistent dynamics samples from the GP within a sequential quadratic programming framework. We highlight the improved reachable set approximation compared to existing methods, as well as real-time feasible computation times, using two numerical examples.
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