End-to-End Deep Fault Tolerant Control

TL;DR

This paper introduces an end-to-end deep learning approach for fault-tolerant control in mechatronic systems, replacing traditional fault detection and controller design with a single recurrent neural network, demonstrated on a spherical inverted pendulum.

Contribution

It presents a novel deep learning-based FTC method that simplifies fault detection and control design into an end-to-end neural network model for nonlinear systems.

Findings

The method effectively handles abrupt sensor faults in simulations.

Experimental results validate the approach on a real spherical inverted pendulum.

The approach outperforms traditional fault detection methods in robustness.

Abstract

PUBLISHED ON IEEE/ASME TRANSACTIONS ON MECHATRONICS, DOI: 10.1109/TMECH.2021.3100150. Ideally, accurate sensor measurements are needed to achieve a good performance in the closed-loop control of mechatronic systems. As a consequence, sensor faults will prevent the system from working correctly, unless a fault-tolerant control (FTC) architecture is adopted. As model-based FTC algorithms for nonlinear systems are often challenging to design, this paper focuses on a new method for FTC in the presence of sensor faults, based on deep learning. The considered approach replaces the phases of fault detection and isolation and controller design with a single recurrent neural network, which has the value of past sensor measurements in a given time window as input, and the current values of the control variables as output. This end-to-end deep FTC method is applied to a mechatronic system composed of a spherical inverted pendulum, whose configuration is changed via reaction wheels, in turn actuated by electric motors. The simulation and experimental results show that the proposed method can handle abrupt faults occurring in link position/velocity sensors. The provided supplementary material includes a video of real-world experiments and the software source code.