Design of a parameter self-tuning PID controller based on an ER-SLP for a variable-frequency air compressor in a rapid deballasting system

The rapid deballasting system is a critical component of the semisubmersible lifting and decommissioning platform of offshore oilfield platforms. During operation, compressed air is injected into the column side ballast chamber by an air compressor to maintain the stability of the platform. The objective of this study is to address the issues of high energy consumption, backward control methods, and low efficiency in power frequency air compressors. To this end, frequency conversion is carried out via simulation. A novel fractional PID controller design, which is based on evidential reasoning (ER) and sequential linear programming (SLP) methodologies, is proposed. The proposed algorithm is evaluated in comparison with power frequency control, a traditional PID controller, and a fractional PID controller within the context of a simulation model established within the Simulink environment. The evaluation is conducted under typical working conditions. The results of the simulation demonstrate that the ER fractional PID controller is more effective in balancing energy consumption and operation time during the operational process and exhibits superior control performance. Specifically, compared to traditional PID and fractional PID controllers, the ER-SLP-based controller achieves 7.2% energy savings over power frequency control while reducing task completion time by 4.7% versus traditional PID. Additionally, it demonstrates enhanced robustness and adaptability under dynamic conditions, addressing pressure fluctuations and computational efficiency challenges in existing methods. This work provides a data-driven, real-time optimization framework for industrial control systems requiring energy-time trade-offs.