Modern automation and control systems applied in energy-conversion units, mechatronic platforms, and industrial processes increasingly operate under conditions characterized by strong nonlinear dynamics, parametric uncertainties, and time-varying external disturbances. These factors significantly degrade the performance of conventional control approaches, including PID and rule-based intelligent controllers, particularly in terms of robustness, transient response, and adaptability. Consequently, the development of advanced control frameworks capable of systematically addressing uncertainty and nonlinear behavior remains a critical challenge in automation and control engineering. This study proposes a quantum-inspired photon–spin control framework for robust automation of nonlinear dynamic systems operating under uncertain conditions. The controlled process is modeled using nonlinear state-space equations representative of automation-oriented dynamic plants. System states are mapped into a photon–spin probabilistic representation, where spin-like state variables and photon-inspired energy encoding enable a superposition-based description of multiple possible system behaviors within a unified control-oriented structure. This representation allows parallel evaluation of alternative control actions under uncertainty. A quantum-inspired inference and decision mechanism based on interference-driven selection logic is employed to identify the most probable optimal control action from the superposed state space. The selected control signal is decoded and applied to the plant through classical actuators, forming a hybrid quantum-inspired/classical feedback control loop that remains fully compatible with standard automation hardware and numerical simulation environments, without requiring physical quantum devices. Simulation studies conducted under significant parametric variations (up to ±15%) and external disturbances demonstrate that the proposed framework achieves approximately 25% reduction in steady-state control error and a 20-30% improvement in transient performance compared to conventional intelligent control strategies. The results confirm the effectiveness and robustness of the proposed approach for next-generation automation and control applications operating under uncertain and dynamically varying conditions.