Differentiable Robust Model Predictive Control
CoRR(2023)
摘要
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, we first present a unifying perspective on
differentiable optimization for control using the implicit function theorem
(IFT), from which existing state-of-the art methods can be derived. Drawing
parallels with differential dynamic programming, the IFT enables the derivation
of an efficient differentiable optimal control framework. The derived scheme is
subsequently paired with a tube-based MPC architecture to facilitate the
automatic and real-time tuning of robust controllers in the presence of large
uncertainties and disturbances. The proposed algorithm is benchmarked on
multiple nonlinear robotic systems, including two systems in the MuJoCo
simulator environment to demonstrate its efficacy.
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关键词
predictive control,robust,model
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