Modeling of cupping suction system based on system identification method - Kavindran, Suresh (2022)
The mathematical modelling for cupping design shown in this study is rather excellent. Using system identification techniques, this paper came up with a way to pick a mathematical model for a cupping suction system that would meet the needs of the controller. The input and output data were used to create this modeling output variable of the cupping suction system is detected by connecting a differential pressure sensor to the cup, while the input variable is determined by the speed of the pump applied in various locations. Many open loop real-time systems exist. The 2nd order fractional model was found to be the best fit for this cupping suction system. Cupping suction plant identification utilizing a nonlinear model based on the modified Sine Cosine Algorithm (mSCA). Moreover, there is a tool in MATLAB called System Identification Toolbox that can help to collect real measurement data samples. The transfer function model also makes use of a continuous-time transfer function. The pole-zero map is used to test the success of the suggested framework in terms of convergence curve responsiveness, output response, and model stability. Cupping suction system is stable in the real testing scenario, as its output was virtually identical to the toolbox-generated estimated output, as opposed to the mSCA approach. For instance, the input of hairy surface was virtually identical to the actual output at 90.75 %. By minimizing integral square errors, fractional order model parameters were optimized (ISE). The results reveal that the better the precision of the modelling cupping system parameter, the lower the error. This is important to ensure the correctness of the real-time output behavior and the accuracy of the output estimate created by mSCA, which produced a somewhat lower result than the MATLAB SID toolbox, which produced more consistent results.