Deterministic model predictive control (MPC), while powerful, is often insufficient for effectively controlling autonomous systems in the real-world. Factors such as environmental noise and model error can cause deviations from the expected nominal performance. Robust MPC algorithms aim to bridge this gap between deterministic and uncertain control. However, these methods are often excessively difficult to tune for robustness due to the nonlinear and non-intuitive effects that controller parameters have on performance. To address this challenge, a unifying perspective on differentiable optimization for control is presented, which enables derivation of a general, differentiable tube-based MPC algorithm. The proposed approach facilitates the automatic and real-time tuning of robust controllers in the presence of large uncertainties and disturbances.

翻译：确定性模型预测控制（MPC）虽功能强大，但通常难以有效控制真实世界中的自主系统。环境噪声和模型误差等因素可能导致系统偏离预期的标称性能。鲁棒MPC算法旨在弥合确定性控制与不确定性控制之间的差距。然而，由于控制器参数对性能的非线性且非直观的影响，这些方法往往难以精细调整鲁棒性。为应对这一挑战，本文提出了一种面向控制的统一可微分优化视角，从而推导出通用的可微分管式MPC算法。该方法能够在存在较大不确定性和扰动的情况下，实现鲁棒控制器的自动实时整定。