In-depth analysis and optimization of electronic closed-loop control for proportional enlarged valves based on SimulationX

This paper conducts an in-depth study on the electronic closed-loop control characteristics of Valvistor valves (a type of proportionally enlarged valve) by leveraging the SimulationX simulation platform. The displacement-area relation-ship of a 25 mm diameter Valvistor valve was accurately determined through the combination of experimental measurements and theoretical analyses. A highly realistic SIMULATIONX model was constructed based on actual parameters, taking into account oil compression, leakage, viscous friction and other key factors. The proportional-integral-derivative parameters were initially calculated using the genetic algorithm and then refined through a series of fine-tuning operations. The results show that the optimized proportional-integral-derivative control significantly improves the dynamic characteristics of the valve: in the step response of the main valve displacement, the rising response time is reduced by 45 milliseconds, the descending step response time is shortened by 20 milliseconds, and there is almost no overshoot compared with the original valve. Considering the variable pressure differences at the valve port in actual working conditions, the parameters of the feed-forward controller were corrected. Through iterative simulation of different pressure differences and feed-forward gain values within the range of 0.5-2 MPa, a parameter curve of the feed-forward controller was obtained, which ensures the valve's displacement output meets actual operation requirements under different pressure conditions. The research results provide important theoretical support and practical guidance for optimizing the performance of Valvistor valves and improving the control accuracy of hydraulic systems. Meanwhile, the limitations of the research (lack of physical experiment verification and insufficient universality) are pointed out, and future research directions (conducting physical experiments, expanding applications in complex systems, optimizing control algorithms) are clarified.