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March 28, 2026IET Control Theory and ApplicationsOpen Access

Reinforcement Learning‐Based Consensus Control for Unknown Nonlinear Multi‐Agent Systems With Sensor Faults

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Authors

VJVahid JamaliBSBehrouz Safarinejadian

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Overview

Demonstrates a fault-tolerant consensus control approach in multi-agent systems, suggesting improved reliability despite sensor faults.

Key Points

  • The research focuses on developing a control method to achieve consensus in multi-agent systems despite sensor faults and unknown dynamics.
  • Introduced a fault-tolerant control approach using reinforcement learning (RL) algorithm.
  • Designed an observer to estimate states and mitigate sensor faults.
  • Employed an actor-critic structure for the learning-based control approach.
  • Proved stability and consensus through theoretical validation.
  • Achieved consensus among agents despite the presence of sensor faults.
  • Maintained system performance as validated through numerical simulations.
  • Effectively compensated for inaccuracies in measurements due to faults.

Cite This Study

Jamali et al. (2026) studied this question.

synapsesocial.com/papers/69c772d98bbfbc51511e3442https://doi.org/10.1049/cth2.70112
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