Fault Detection and Fault-Tolerant Control of Permanent Magnet Linear Motors Using an Emotional Learning-Based Neural Network and a Linear Extended State Observer

This paper presents a unified framework for reliable motion control of permanent magnet linear motors (PMLMs) by integrating fault detection (FD) and fault-tolerant control (FTC). The framework combines a brain emotional learning-based intelligent controller (BELBIC) with a linear extended state observer (LESO) to enable rapid detection and mitigation of abrupt and incipient faults, as well as disturbances and sensor noise that degrade tracking accuracy and system reliability. The LESO is employed to estimate unknown dynamics and lumped disturbances and to generate residuals for reliable fault detection, while BELBIC provides adaptive and robust control actions without requiring prior knowledge of system parameters or explicit fault models. Extensive simulation studies under actuator faults, system dynamics faults, external disturbances, and measurement noise are conducted. Comparative evaluations with benchmark approaches demonstrate improved fault detection speed, tracking accuracy, and robustness of the proposed framework, highlighting its potential for enhancing reliability and operational continuity in high-precision industrial applications.