# Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control
## 1 Introduction
[23] combined fuzzy logic control and Particle Swarm Optimization algorithm with the neural network model to enhance the stability and performance of two-wheeled wheelchair systems. [PERSON] et al. [24] analyzed the performance of five algorithms, namely Equilibrium Optimizer ([PERSON]), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Differential Evolution (DE), and Genetic Algorithm (GA), in fine-tuning the gains of a proportional-integral-derivative (PID) controller to control the speed of a DC motor. All of these five algorithms are able to provide the optimal gains, but their performance is inferior to that of the Fuzzy Logic Controller. [PERSON] et al. [25] proposed taking the parallel hybrid ship as the research object[PERSON] They designed the two-layer fuzzy logic control strategy, the dynamic programming algorithm, and the improved fuzzy logic control strategy using Simulink. Then, co-simulation validation was carried out from multiple aspects. The results showed that the improved fuzzy logic control strategy can achieve energy conservation and emission reduction and also ensure real-time stability[PERSON]  
In the process of investigating the DPV with the HSVB as the pilot section, it was found that the existing control methods will cause large pressure fluctuations in the two pilot control chambers of the main valve during the positioning process. This in turn will cause high-frequency oscillation in the main spool, leading to the reduction in control accuracy and stability of DPV. [PERSON] and [PERSON] have proposed a SPID controller optimized by NSCA for AVR applications, showing excellent stability and frequency response characteristics in contrast to traditional PID controllers. Although the control method proposed by [PERSON] and [PERSON] has demonstrated remarkable control efficacy when applied to the AVR system, the control performance of the SPID controller may not be as outstanding for complex time-varying systems [26]. The fractional PID controller has drawbacks such as difficult parameter tuning due to the increased number of parameters compared to the classical PID controller, the complexity of tuning in the non-linear version with eight parameters, potential instability when the order exceeds 2, and limitations in handling extremely complex systems and being restricted by practical conditions in some applications [27]. The existing data-driven neuroendocrine-PID using SPSA as the tuning/optimization tool has disadvantages like inability to find the optimal value due to memory-less optimization, possible un-stable convergence, step size dependence on gain sequences that decrease with more iterations, and lacking energy to readjust step size for new optimal solutions when disturbances, etc., occur during tuning [28]. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity [29]. The physical system under investigation in this paper exhibits a high degree of non-linearity, time-variability, and un-certainty. Certain factors may also undergo alterations in accordance with the passage of time and variations in the operating conditions. For instance, the variation in the coil temperature of the HSV can exert an impact on its resistance and capacitance, along with the fluctuations in the oil supply pressure of the pilot stage, and so forth. The fuzzy PID controller is capable of handling such intricate relationships by means of fuzzy rules. Consequently, the fuzzy intelligent PID controller is selected. Aiming at this problem, an oscillation suppression method based on fuzzy intelligent proportional-integral-derivative (PID) control is proposed in this study, which can effectively suppress the spool oscillation, improve the response speed and control accuracy of the main spool, and provide a guarantee for the wide application of DPV.