End-to-End Deep Fault Tolerant Control

Daulet Baimukashev, Bexultan Rakhim, Matteo Rubagotti, Huseyin Atakan Varol

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalIEEE/ASME Transactions on Mechatronics
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Deep learning
  • fault detection and isolation
  • fault tolerant control
  • Mathematical model
  • mechatronic systems
  • Mechatronics
  • Observability
  • Observers
  • recurrent neural networks
  • Recurrent neural networks
  • Robot sensing systems
  • Training

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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