Model Predictive Fault‐Tolerant Control for Double‐Toothed Roller Crushers

The double‐tooth roll crusher, commonly driven by an induction motor, often faces sensor and actuator malfunctions. To address these, this study proposes a model predictive fault‐tolerant control framework to enhance system reliability and robustness. Initially, based on the system's mathematical model, a state and unknown input observer is developed. This observer effectively detects current sensor faults and isolates unknown disturbances, maintaining system functionality even with single‐sensor operation. Additionally, a velocity observer is designed using the estimated state data. Secondly, system faults are treated as uncertainties, leading to the development of a disturbance observer with fewer tunable parameters. This observer identifies actuator failures by analyzing disturbance values, with its convergence validated theoretically. This enables rapid fault detection and mitigation, reducing performance degradation. Finally, a model predictive torque controller is introduced to improve torque regulation. Experimental results demonstrate the method's strong fault‐tolerant capabilities and control effectiveness across various fault scenarios.