Neural Network Output-Feedback Consensus Fault-Tolerant Control for Nonlinear Multiagent Systems With Intermittent...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Nonlinear multiagent systems (NMASs) face challenges with fault tolerance due to actuator faults.
• Intermittent faults and unmeasured states complicate control design in NMASs.
• Existing methods often struggle with nonstrict-feedback structures and algebraic loops.

Purpose of the Study:

• To develop a distributed adaptive neural network (NN) based fault-tolerant control (FTC) strategy.
• To address intermittent actuator faults in nonstrict-feedback NMASs.
• To overcome the algebraic-loop issue in output-feedback control.

Main Methods:

• Approximation of nonlinear dynamics using neural networks (NNs).
• Design of a NN state-observer for unmeasured state estimation.
• Co-design of a novel distributed output-feedback adaptive FTC controller.
• Lyapunov stability theory for system analysis.

Main Results:

• A robust FTC scheme for NMASs with intermittent actuator faults.
• Successful estimation of unmeasured states via NN state-observer.
• Resolution of the algebraic-loop problem in the control design.
• Validated effectiveness through numerical simulations and practical examples.

Conclusions:

• The proposed distributed adaptive NN-based FTC is effective for nonstrict-feedback NMASs.
• The method ensures system stability and fault tolerance under intermittent actuator faults.
• The approach offers a viable solution for complex networked control systems.