Enhancing fault tolerance and robustness in chemical process control using learning-based PID design

Abstract Effective control of industrial systems remains challenging due to inherent non-linearities, parameter uncertainties, external disturbances, and sensor-actuator faults. These complexities hinder the reliability of closed-loop performance. Conventional strategies often fail to ensure robustness in the face of operational variability. To address these limitations, this study introduces a learning-enabled control approach that integrates predictive modeling and robust tuning to enhance tracking accuracy, improve fault tolerance, and ensure stability in uncertain environments. The proposed control framework combines a learning-based predictive model with a constrained optimization routine to tune the parameters of the PID controller. The learning component enables the controller to adapt dynamically to system changes and potential faults. The optimization process seeks to maximize closed-loop bandwidth while imposing bounds on control actions to prevent actuator saturation. To ensure robust stability under model uncertainty and dynamic variations, disk margin-based analysis is incorporated into the design, providing frequency-domain guarantees against perturbations. The framework is validated against various benchmark processes, under varying set points, disturbances, and fault conditions. The proposed controller exhibits improved closed-loop performance compared to traditional controllers, achieving lower overshoot and faster settling times. It remains robust in the presence of sensor faults, maintaining accurate and stable control. Disk margin analysis confirms that the Nyquist contours stay well within the allowable stability region, indicating robust performance. The framework achieves stable operation under uncertainty as validated through Monte Carlo analysis. The novelty of this work lies in the development of a hybrid framework that exploits data-driven prediction and metaheuristic optimization to achieve fault-tolerant and stability-assured control of nonlinear process systems. By integrating predictive learning with optimization-based robustness tuning, the proposed framework bridges the gap between adaptability and stability, demonstrating superior reliability under parametric uncertainties and faults. Overall, the results establish that the learning-based control strategy offers significant performance gains and reliability enhancements over existing methods in managing complex, uncertainty-prone industrial processes.