Quantum-Inspired Photon-Spin Control Framework for Robust Automation of Nonlinear Dynamic Systems Under Uncertain Operating Conditions

Robust control of nonlinear dynamic systems remains challenging because parametric uncertainty and external disturbances can significantly degrade tracking accuracy and transient performance. This study proposes a Quantum-Inspired Photon–Spin Control Framework (QPSCF) that integrates probabilistic state representation and interference-inspired decision-making directly into the online feedback control process while remaining fully executable on classical computing platforms. Unlike quantum-inspired approaches primarily used for offline controller tuning or heuristic optimization, the proposed framework represents multiple candidate operating states probabilistically and adaptively evaluates competing control actions according to current process conditions. The QPSCF was evaluated on a nonlinear benchmark system under parameter variations of up to ±15% and a 10% external disturbance and compared with conventional PID and Mamdani fuzzy controllers under identical simulation conditions. The proposed controller achieved a settling time of 40.70 s, an overshoot of 0.15%, an RMSE of 4.83, an IAE of 121.54, and an ISE of 1652.41. Compared with PID control, QPSCF reduced RMSE, IAE, and ISE by 19.6%, 29.6%, and 41.9%, respectively. It also reduced the maximum disturbance-induced deviation from 1.80 °C to 0.55 °C and the recovery time from 20.30 s to 5.90 s. These results demonstrate that direct probabilistic decision-making within the feedback loop can improve tracking accuracy and disturbance rejection in nonlinear systems under uncertainty.

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