# Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control

arXiv: https://arxiv.org/abs/2008.04403 · HTML: https://mlsanity.com/paper/2008.04403/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning](https://mlsanity.com/paper/2409.14435/) (2024)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)
- [A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances](https://mlsanity.com/paper/2305.12543/) (2023)

## Abstract (arXiv)

A desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations. An adaptive controller does not require optimal control policies to be enumerated for possible faults. Instead it can approximate one in real-time. We present two adaptive fault-tolerant control schemes for a discrete time system based on hierarchical reinforcement learning. We compare their performance against a model predictive controller in presence of sensor noise and persistent faults. The controllers are tested on a fuel tank model of a C-130 plane. Our experiments demonstrate that reinforcement learning-based controllers perform more robustly than model predictive controllers under faults, partially observable system models, and varying sensor noise levels.