1. Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples
- Author
-
Mittal, Mayank, Gallieri, Marco, Quaglino, Alessio, Salehian, Seyed Sina Mirrazavi, and Koutník, Jan
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Neural and Evolutionary Computing ,Electrical Engineering and Systems Science - Systems and Control - Abstract
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. The proposed algorithm alternatively learns the terminal cost and updates the MPC parameters according to a stability metric. The terminal cost is constructed as a Lyapunov function neural network with the aim of recovering or extending the stable region of the initial demonstrator using a short prediction horizon. Theorems that characterize the stability and performance of the learned MPC in the bearing of model uncertainties and sub-optimality due to function approximation are presented. The efficacy of the proposed algorithm is demonstrated on non-linear continuous control tasks with soft constraints. The proposed approach can improve upon the initial demonstrator also in practice and achieve better stability than popular reinforcement learning baselines.
- Published
- 2020