1 research outputs found
Learning Stable Adaptive Explicit Differentiable Predictive Control for Unknown Linear Systems
We present differentiable predictive control (DPC), a method for learning
constrained adaptive neural control policies and dynamical models of unknown
linear systems. DPC presents an approximate data-driven solution approach to
the explicit Model Predictive Control (MPC) problem as a scalable alternative
to computationally expensive multiparametric programming solvers. DPC is
formulated as a constrained deep learning problem whose architecture is
inspired by the structure of classical MPC. The optimization of the neural
control policy is based on automatic differentiation of the MPC-inspired loss
function through a differentiable closed-loop system model. This novel solution
approach can optimize adaptive neural control policies for time-varying
references while obeying state and input constraints without the prior need of
an MPC controller. We show that DPC can learn to stabilize constrained neural
control policies for systems with unstable dynamics. Moreover, we provide
sufficient conditions for asymptotic stability of generic closed-loop system
dynamics with neural feedback policies. In simulation case studies, we assess
the performance of the proposed DPC method in terms of reference tracking,
robustness, and computational and memory footprints compared against classical
model-based and data-driven control approaches. We demonstrate that DPC scales
linearly with problem size, compared to exponential scalability of classical
explicit MPC based on multiparametric programming.Comment: 11 pages. Code for reproducing our experiments is available at:
https://github.com/pnnl/deps_arXiv2020