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Security data-driven iterative learning control for unknown nonlinear systems with hybrid attacks and fading

Yanling Yin1, Wei Yu2, Xuhui Bu3

  • 1Research Center for Energy Economics, School of Business Administration, Henan Polytechnic University, Jiaozuo 454003, China.

ISA Transactions
|February 7, 2022
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Summary

This study addresses secure data-driven control for nonlinear systems facing hybrid attacks. New methods ensure system stabilization despite unknown dynamics and communication interference.

Keywords:
Data-driven controlFading measurementsHybrid attacksIterative learning controlSecurity control

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Area of Science:

  • Control Engineering
  • Cybersecurity
  • Nonlinear Systems

Background:

  • Stabilizing nonlinear systems with unknown dynamics is challenging.
  • Networked systems are vulnerable to hybrid attacks injecting false data.
  • Iterative learning control offers potential for system improvement.

Purpose of the Study:

  • To develop secure data-driven control strategies for nonlinear systems.
  • To address challenges posed by unknown dynamics and hybrid network attacks.
  • To ensure system stabilization under iterative learning schemes.

Main Methods:

  • Transforming the plant into an iteration-related dynamic data-model.
  • Designing two data-driven control methods using incomplete input-output signals.
  • Employing a compensation scheme with increasing gains.

Main Results:

  • Theoretical analysis confirms the effectiveness of the proposed algorithms.
  • The influence of stochastic issues on system performance is evaluated.
  • Numerical simulations and a practical example demonstrate control validity.

Conclusions:

  • The proposed data-driven control methods effectively stabilize nonlinear systems.
  • The approach is robust against hybrid attacks and stochastic uncertainties.
  • Validated through simulations and agricultural vehicle tracking, proving practical applicability.