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Set membership fault detection for nonlinear dynamic systems

Set membership fault detection for nonlinear dynamic systems

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In this chapter, an innovative approach to fault detection for nonlinear dynamic systems is proposed, based on the recently introduced quasi-local set membership-identification method, overcoming some relevant issues proper of the “classical” techniques. The approach is based on the direct identification from experimental data of a suitable filter and related uncertainty bounds. These bounds are used to detect when a change (e.g., a fault) has occurred in the dynamics of the system of interest. The main advantage of the approach compared to the existing methods is that it avoids the utilization of complex modeling and filter design procedures, since the filter/observer is directly designed from data. Other advantages are that the approach does not require to choose any threshold (as typically done in many “classical” techniques), and it is not affected by under-modeling problems. An experimental study regarding fault detection for a drone actuator is finally presented to demonstrate the effectiveness of the proposed approach.

Chapter Contents:

  • 12.1 Introduction
  • 12.2 Nonlinear set membership fault detection
  • 12.2.1 Problem formulation
  • 12.3 Nonlinear set membership identification: global approach
  • 12.3.1 Interval estimates
  • 12.4 Nonlinear set membership identification: local approach
  • 12.4.1 Interval estimates
  • 12.4.1.1 Noise bounded in l2 norm
  • 12.4.1.2 Noise bounded in l∞ norm
  • 12.4.2 Local approach—identification algorithms
  • 12.5 Nonlinear set membership identification: quasi-local approach
  • 12.5.1 Interval estimates
  • 12.6 Parameter estimation and adaptive set membership model
  • 12.6.1 Parameter estimation
  • 12.6.2 Adaptive set membership model
  • 12.7 Summary of set membership fault-detection procedure
  • 12.8 Example: fault detection for a drone actuator
  • 12.8.1 Experimental setup
  • 12.8.2 Nonlinear set membership fault detection
  • 12.8.2.1 Global approach
  • 12.8.2.2 Quasi-local approach
  • 12.8.2.3 Local approach
  • 12.9 Conclusions
  • References

Inspec keywords: actuators; identification; remotely operated vehicles; fault diagnosis; control system synthesis; set theory; observers; nonlinear dynamical systems

Other keywords: set membership fault detection; uncertainty bounds; filter design; quasilocal set membership-identification; nonlinear dynamic systems; fault detection; direct identification; drone actuator; observer design

Subjects: Control system analysis and synthesis methods; Nonlinear control systems; Simulation, modelling and identification; Actuating and final control devices; Combinatorial mathematics

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