Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples

Understand

With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides an opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account.

• In many real-world and industrial applications, it is typical to have an existing control strategy, for instance, execution from a human operator.
• The objective of this work is to improve upon this unknown, safe but suboptimal policy by learning a new controller that retains safety and stability.
• Learning how to be safe is achieved directly from data and from a knowledge of the system constraints.