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Related Experiment Video

Updated: May 9, 2025

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Learning observer-based fault-tolerant control for semi-Markov jumping systems with probabilistic-memory

Qilong Xie¹, Yunliang Wang¹, Jun Cheng¹

¹School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China.

ISA Transactions

|May 6, 2025

Summary

This study introduces a new fault-tolerant control method for semi-Markov jumping systems using a probabilistic-memory event-triggered protocol. It achieves stability and reduces communication load with rapid fault estimation.

Keywords:

Deterministic signal Fault-tolerant control Non-differentiable sensor faults Semi-Markov jumping systems

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Area of Science:

Control Systems Engineering
Stochastic Systems Analysis
Networked Control Systems

Background:

Semi-Markov jumping systems (SMJSs) are crucial for modeling systems with state-dependent switching behaviors.
Observer-based fault-tolerant control is essential for maintaining system stability and performance under component failures.
Event-triggered communication protocols aim to reduce network load but often face challenges with stability guarantees and network delays.

Purpose of the Study:

To develop a learning-based observer for fault-tolerant control of SMJSs.
To design a novel probabilistic-memory event-triggered protocol (PMETP) that accommodates network delays and reduces communication burden.
To ensure system stability and performance despite sensor faults and communication constraints.

Main Methods:

Utilizing a piecewise-homogeneous semi-Markov process governed by an average-dwell-time signal to model system dynamics.
Implementing a novel PMETP that leverages historical internal variables and accounts for random network delays.
Introducing a learning-based observer for accurate and rapid estimation of non-differentiable sensor faults.

Main Results:

The proposed PMETP significantly reduces data transmissions without compromising system stability.
The learning-based observer effectively estimates sensor fault errors, even for non-differentiable faults.
The integrated fault-tolerant control strategy demonstrates robust performance for SMJSs under considered conditions.

Conclusions:

The developed approach provides an effective solution for fault-tolerant control in SMJSs with reduced communication requirements.
The combination of PMETP and learning-based observers offers a promising direction for future networked control system designs.
The method successfully balances performance, stability, and communication efficiency in complex dynamic systems.