  In this paper, we introduce a new class of parameterized controllers, drawing
inspiration from Model Predictive Control (MPC). The controller resembles a
Quadratic Programming (QP) solver of a linear MPC problem, with the parameters
of the controller being trained via Deep Reinforcement Learning (DRL) rather
than derived from system models. This approach addresses the limitations of
common controllers with Multi-Layer Perceptron (MLP) or other general neural
network architecture used in DRL, in terms of verifiability and performance
guarantees, and the learned controllers possess verifiable properties like
persistent feasibility and asymptotic stability akin to MPC. On the other hand,
numerical examples illustrate that the proposed controller empirically matches
MPC and MLP controllers in terms of control performance and has superior
robustness against modeling uncertainty and noises. Furthermore, the proposed
controller is significantly more computationally efficient compared to MPC and
requires fewer parameters to learn than MLP controllers. Real-world experiments
on vehicle drift maneuvering task demonstrate the potential of these
controllers for robotics and other demanding control tasks.
