Quantum-Inspired Fuzzy Inference-Based Intelligent Control of Nonlinear Technological Processes

Nonlinear technological processes are difficult to control because of strong nonlinear dynamics, parameter uncertainties, transport delays, and external disturbances, which can degrade the performance of conventional control strategies. This study proposes a Quantum-Inspired Fuzzy Inference Controller (QIFIC) that combines Mamdani fuzzy reasoning with an online adaptive probabilistic decision mechanism. Unlike conventional fuzzy controllers, which directly aggregate activated rules into a deterministic output, the proposed method preserves multiple candidate control actions and dynamically adjusts their relative probabilities according to the tracking error and its variation. The updated probabilities are combined with fuzzy-rule firing strengths to generate the final control signal, enabling online adaptation without modifying or retraining the original fuzzy knowledge base. A conditional Lyapunov-based analysis is used to establish the uniform ultimate boundedness of the tracking error under bounded modeling uncertainties and time-varying disturbances within the considered operating region. The proposed controller is evaluated using a nonlinear boiler temperature-control benchmark and quantitatively compared with PID and classical fuzzy controllers, while an adaptive fuzzy controller is included as an additional methodological benchmark for positioning the proposed adaptation strategy. Comparative simulations demonstrate improved transient response, disturbance rejection, and control smoothness, confirming the effectiveness of the proposed online probability-weighted inference mechanism.

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