# Neuroadaptive Distributed Event-triggered Control of Networked Uncertain Pure-feedback Systems with Polluted Feedback

## TL;DR

This paper develops a neural network-based distributed event-triggered control method for uncertain pure-feedback multi-agent systems with polluted feedback, ensuring stability and consensus despite sensor failures and non-affine system structures.

## Contribution

It introduces a novel NN-based control strategy with a replacement policy that handles non-differentiability and sensor pollution without fault diagnosis, advancing control of complex multi-agent systems.

## Key findings

- All internal signals are semi-globally uniformly ultimately bounded.
- Subsystem outputs reach consensus without infinitely fast triggering.
- The proposed method is validated through numerical simulations.

## Abstract

This paper investigates the distributed event-triggered control problem for a class of uncertain pure-feedback nonlinear multi-agent systems (MASs) with polluted feedback. Under the setting of event-triggered control, substantial challenges exist in both control design and stability analysis for systems in more general non-affine pure-feedback forms wherein all state variables are not directly and continuously available or even polluted due to sensor failures, and thus far very limited results are available in literature. In this work, a nominal control strategy under regular state feedback is firstly developed by combining neural network (NN) approximating with dynamic filtering technique, and then a NN-based distributed event-triggered control strategy is proposed by resorting to a novel replacement policy, making the non-differentiability issue arising from event-triggering setting completely circumvented. Besides, the sensor ineffectiveness is accommodated automatically without using fault detection and diagnosis unit or controller reconfiguration. It is shown that all the internal signals are semi-globally uniformly ultimately bounded (SGUUB) with the aid of several vital lemmas, while the outputs of all the subsystems reaching a consensus without infinitely fast execution. Finally, the efficiency of the developed algorithm are verified via numerical simulation.