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July 26, 2026Guidance Navigation and ControlOpen Access

Fault-tolerant optimized consensus control for a class of second-order nonlinear multi-agent systems subjected to actuator failures

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Authors

LZLu ZhangXi'an Aeronautical UniversityWCWeihao CuiQilu University of TechnologyDYDengxiu YuMinistry of Public Security of the People's Republic of China

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Overview

Randomized trial investigates optimized fault-tolerant control in nonlinear multi-agent systems, suggesting improvements for systems engineering.

Key Points

  • This work aims to develop a fault-tolerant optimized consensus control strategy for second-order nonlinear multi-agent systems affected by actuator failures.
  • Implemented a reinforcement learning strategy to avoid analytical solutions of the Hamilton-Jacobi-Bellman equation.
  • Integrated an adaptive estimation algorithm for unknown actuator fault parameters.
  • Developed a simplified reinforcement learning algorithm based on the negative gradient of a positive function.
  • The proposed control strategy effectively regulates both position and velocity states to achieve consensus.
  • Theoretical analysis confirms the feasibility of the fault-tolerant control approach.
  • Numerical simulations support the successful implementation of the adaptive estimation within the optimized control scheme.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a65a337d3aea3239cd7666ahttps://doi.org/10.1142/s273748072640011x
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Also Consider

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  1. 1Reinforcement Learning‐Based Consensus Control for Unknown Nonlinear Multi‐Agent Systems With Sensor Faults2026
  2. 2Event‐triggered adaptive fault‐tolerant control for heterogeneous nonlinear multi‐agent systems2024 · 3 citations
  3. 3Data-driven fault-tolerant consensus control for nonlinear multi-agent systems under sensor and actuator faults2025
  4. 4Prespecified-Performance-Driven Triggering Consensus of Nonlinear Multiagent Systems With Unknown Actuator Faults2025
  5. 5Adaptive resilient containment control using reinforcement learning for nonlinear stochastic multi-agent systems under sensor faults2024