Hybrid meta-heuristics neural network-based PID tuning for robust and fault-tolerant control of integrating processes

Abstract

Accurate control of integrating and non-self-regulating processes remains a persistent challenge in process industries due to their inherent open-loop instability, slow response characteristics, and high sensitivity to parametric uncertainties and external disturbances. These characteristics often render conventional proportional-integral-derivative (PID) controllers inadequate, particularly under varying operating conditions or fault scenarios. To address these limitations, this study introduces a novel hybrid PID tuning framework that integrates the global optimization capability of the Harris Hawks Optimization (HHO) algorithm with a neural network-based supervisory model. The neural network is trained to dynamically estimate admissible bounds for key performance indices such as integral absolute error (IAE), integral squared error (ISE), and integral time-weighted absolute error (ITAE) based on process behavior. These bounds serve as adaptive constraints in the optimization phase, allowing the tuning mechanism to remain process-specific and responsive to variations, faults, and non-linearities. The overall controller design is formulated as a constrained multi-objective optimization problem, where the objectives include minimizing control errors while simultaneously satisfying robustness, stability, and performance constraints. Unlike traditional fixed-rule or purely heuristic-based tuning techniques, the proposed approach enables online adaptation to disturbances and faults by leveraging real-time feedback from the neural network. This enhances both robustness and fault tolerance across a wide range of operating scenarios. The effectiveness of the proposed method is rigorously evaluated through extensive simulations on several benchmark integrating processes under both nominal and faulty conditions, including sensor and actuator faults. Comparative analysis with recent methods confirms that the proposed controller offers superior tracking accuracy, faster settling time, and enhanced robustness. In addition, the robustness of the closed-loop system is graphically validated using frequency-domain magnitude plots under multiplicative input and output uncertainties. These results confirm the practical value and innovative nature of the proposed intelligent hybrid tuning strategy.