Algorithmic design and implementation considerations of deep MPC
This work is incremental, targeting control systems researchers by refining Deep MPC approaches for improved safety and performance in learning-based control.
The paper addresses the implementation challenges of Deep MPC, focusing on distributing control authority between a neural network and an MPC controller to handle model uncertainties and constraints, and demonstrates that poor distribution choices can lead to performance issues through a numerical experiment on a four-wheeled skid-steer dynamics.
Deep Model Predictive Control (Deep MPC) is an evolving field that integrates model predictive control and deep learning. This manuscript is focused on a particular approach, which employs deep neural network in the loop with MPC. This class of approaches distributes control authority between a neural network and an MPC controller, in such a way that the neural network learns the model uncertainties while the MPC handles constraints. The approach is appealing because training data collected while the system is in operation can be used to fine-tune the neural network, and MPC prevents unsafe behavior during those learning transients. This manuscript explains implementation challenges of Deep MPC, algorithmic way to distribute control authority and argues that a poor choice in distributing control authority may lead to poor performance. A reason of poor performance is explained through a numerical experiment on a four-wheeled skid-steer dynamics.