Rastko Selmic, Ph'D'

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Actuator Fault Detection in Nonlinear Systems
Using Neural Networks

• Rastko Selmic, Ph.D.
• Department of Electrical Engineering and
  Institute for Micromanufacturing
• Louisiana Tech University
• Ruston, LA 71272, USA
• Email rselmic_at_latech.edu
• Web http//www2.latech.edu/rselmic/

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Contents

• Introduction
• Problem Formulation
• Actuator Fault Detection, Fault Dynamics, and
  Fault Detectability
• Two cases considered
• State feedback
• Output feedback
• Simulation Results
• Conclusion
• Other projects, ideas, etc.

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Introduction

• Collaborative work with Marios Polycarpou and
  Thomas Parisini
• An actuator fault identification in unknown,
  input-affine, nonlinear systems using neural
  networks is presented
• Two cases are considered state feedback and
  output feedback case
• Neural net tuning algorithms and identifier have
  been developed using the Lyapunov approach
• A rigorous detectability condition is given for
  actuator faults relating the actuator desired
  input signal and neural net-based observer
  sensitivity
• Simulation results are presented to illustrate
  the detectability criteria and fault detection in
  nonlinear systems.

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Questions to be Answered

• What kind of actuator faults can be detected?
• Under what conditions faults are detectable using
  NN identifiers?
• If faults are not presently detectable, how
  identifier parameters need to be adjusted in
  order to detect the faults?

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Problem Formulation
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Problem Formulation
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Case I State Feedback
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A NN System Observer
Figure 1. NN system observer fault identifier.
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NN Tuning Law
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Stability Analysis
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The State Observer Error
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Dynamics of a Fault
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Detectability of Actuator Faults
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Case II Output Feedback
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A NN Observer
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A NN Observer
Figure 2. NN system observer fault identifier.
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NN Observer Tuning Law
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Dynamics of a Fault
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Detectability of Actuator Faults
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Detectability of Actuator Faults

• The result relates observer parameters, i.e. NN
  weights, with fault detectability and the
  actuator control signal
• It also shows when actuator faults can not be
  detected or what needs to be done with NN
  observer to improve sensitivity.

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Simulation Example
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Simulation Example
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Simulation Example
System state observer errors e1(t) (full line)
and e2(t) (dotted line).
Norm of the error e(t).
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Simulation Example
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Simulation Example
Actuator fault at t5sec norm of the error e(t).
Actuator fault at t5sec system state observer
errors e1(t) (full line) and e2(t) (dotted line).
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Conclusions

• It is shown how neural net-based system can be
  used for actuator fault detection in unknown,
  nonlinear, input-affine systems.
• Stable neural net tuning laws are given and
  estimate on the state observer error is provided
  using Lyapunov approach.
• Sufficient conditions for actuator fault
  detectability are presented.
• An open research problem is to combine active
  fault detection methods in case detectability
  conditions are not satisfied.

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Intelligent Sensors and Actuators Group

• Research interests
• Wireless sensor networks for chemical agents
  monitoring
• Suboptimal coverage control missions in mobile
  sensor networks.
• Intelligent actuator control using neural
  networks
• Actuators and sensors failure detection and
  compensation
• Intelligent wireless sensor networks
• Group members Dr. Rastko Selmic, 3 Ph.D.
  students, 4 M.S. students, and 2 undergraduate
  students.
• The group has two laboratories with several
  control system setups, sensors, wireless sensor
  nodes, two mobile robots, 11 PC computers.

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Intelligent Sensors and Actuators Laboratory

• The newest lab in EE 11 PC computers, 8 control
  system experimental setups, sensors, wireless
  sensor nodes, two mobile robots, 2 oscilloscopes.

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Smart Actuator Control Using IEEE 1451 Standard

• Develop a smart actuator control that is
  compatible with IEEE 1451 standard for smart
  transducers.
• The concept allows for intelligent control based
  on data and metadata gathered by the network of
  smart sensors.
• Control action depends on sensor data and
  information stored in TEDS and HEDS.

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Testbed Development Chemical Agent Monitoring

• Developed a chemical sensor board for WSN
  applications based on Xbow motes.
• Sensor nodes monitor for carbon monoxide (CO),
  nitrogen dioxide (NO2), and methane (CH4).
• Research problem a suboptimal sensor network
  coverage of the area of interest while providing
  quasi real-time tracking and monitoring of the
  focus area observation space.

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Simulation Tool for Coverage Control in Mobile
Sensor Networks

• Simulation tool is needed to experiment with
  variety of algorithms for sensor node deployment
  under localization and network connectivity
  conditions.
• Development based on C (optimization, network
  conditions) and C (GUI).
• C language chosen so simulation can be ported to
  High Performance Computing machines in case it is
  needed for very large networks.
• Examples of different scenarios in sensor network
  coverage control uniform coverage, focused
  coverage, balanced coverage control of sensor
  nodes.

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Thank you! Any questions?