Optimized Liquid Level Control in Near-Infrared Spectroscopy-Based Liquid Phase Detection Equipment Using a PSSA-PID Approach

To address the time-delay and nonlinear characteristics of liquid level control in near-infrared spectroscopybased liquid phase detection equipment, as well as the pipeline cavitation issues caused by improper sample pump speed settings during sample delivery-which may result in air bubble retention within the cuvette and subsequently degrade spectral data quality-a dual-buffer bottle sample delivery system model was established.A Proportional-Integral-Derivative (PID) controller was designed, and an enhanced hybrid algorithm integrating the Particle Swarm Optimization (PSO) algorithm and the Sparrow Search Algorithm (SSA) was proposed.The hybrid algorithm, referred to as the Adaptive Chaotic Mapping Particle Swarm Sparrow Algorithm (ACM-PSSA), incorporates Tent chaotic mapping for population initialization, a nonlinear cosine-based adaptive sparrow classification strategy, and a master-slave optimization mechanism wherein SSA performs global exploration and PSO executes local exploitation to optimize PID parameters.Simulation results demonstrate that ACM-PSSA outperforms traditional SSA and PSO across six benchmark test functions in terms of convergence speed, accuracy, and stability.When applied to the liquid level control of the dual-buffer bottle system, the optimized controller achieved a rise time of 0.188 seconds, a settling time of 1.211 seconds, and an overshoot reduced to 20.98%.By leveraging chaotic mapping, adaptive classification, and a master-slave optimization framework, ACM-PSSA effectively overcomes the limitations of conventional SSA and PSO, significantly enhancing the efficiency of PID parameter optimization and the overall control performance of the dual-buffer bottle sample delivery system.