Modeling of cupping suction system based on system identification method

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

Safe Experimentation Dynamics Algorithm for Identification of Cupping Suction Based on the Nonlinear Hammerstein Model

The use of cupping therapy for various health benefits has increased in popularity recently. Potential advantages of cupping therapy include pain reduction, increased circulation, relaxation, and skin health. The increased blood flow makes it easier to supply nutrients and oxygen to the tissues, promoting healing. Nevertheless, the effectiveness of this technique greatly depends on the negative pressure's ability to create the desired suction effect on the skin. This research paper suggests a method to detect the cupping suction model by employing the Hammerstein model and utilizing the Safe Experimentation Dynamics (SED) algorithm. The problem is that the cupping suction system experiences pressure leaks and is difficult to control. Although, stabilizing the suction pressure and developing an effective controller requires an accurate model. The research contribution lies in utilizing the SED algorithm to tune the parameters of the Hammerstein model specifically for the cupping suction system and figure out the real system with a continuous-time transfer function. The experimental data collected for cupping therapy exhibited nonlinearity attributed to the complex dynamics of the system, presenting challenges in developing a Hammerstein model. This work used a nonlinear model to study the cupping suction system. Input and output data were collected from the differential pressure sensor for 20 minutes, sampling every 0.1 seconds. The single-agent method SED has limited exploration capabilities for finding optimum value but excels in exploitation. To address this limitation, incorporating initial values leads to improved performance and a better match with the real experimental observations. Experimentation was conducted to find the best model parameters for the desired suction pressure. The therapy can be administered with greater precision and efficacy by accurately identifying the suction pressure. Overall, this research represents a promising development in cupping therapy. In particular, it has been demonstrated that the proposed nonlinear Hammerstein models improve accuracy by 84.34% through the tuning SED algorithm

Modelling of cupping suction system based on system identification method

The detection of cupped suction system plants using a standard model based on a modified Sine Cosine Algorithm (mSCA) is presented in this research. According to the findings, the mSCA-based technique can produce optimal parameters of model that provides an identified output response comparable to the actual experiment's cupping suction system output, with an integral square error for various random input surfaces and its objective function. The input and output data were used to create this modelling output variable of the cupping suction system detected by connecting the differential pressure sensor to the cup. In contrast, the input variable is determined by the speed of the pump applied in various locations. The transfer function model also makes use of the continuous-time transfer function

Safe experimentation dynamics algorithm for identification of cupping suction based on the nonlinear Hammerstein model

The use of cupping therapy for various health benefits has increased in popularity recently. Potential advantages of cupping therapy include pain reduction, increased circulation, relaxation, and skin health. The increased blood flow makes it easier to supply nutrients and oxygen to the tissues, promoting healing. Nevertheless, the effectiveness of this technique greatly depends on the negative pressure's ability to create the desired suction effect on the skin. This research paper suggests a method to detect the cupping suction model by employing the Hammerstein model and utilizing the Safe Experimentation Dynamics (SED) algorithm. The problem is that the cupping suction system experiences pressure leaks and is difficult to control. Although, stabilizing the suction pressure and developing an effective controller requires an accurate model. The research contribution lies in utilizing the SED algorithm to tune the parameters of the Hammerstein model specifically for the cupping suction system and figure out the real system with a continuous-time transfer function. The experimental data collected for cupping therapy exhibited nonlinearity attributed to the complex dynamics of the system, presenting challenges in developing a Hammerstein model. This work used a nonlinear model to study the cupping suction system. Input and output data were collected from the differential pressure sensor for 20 minutes, sampling every 0.1 seconds. The single-agent method SED has limited exploration capabilities for finding optimum value but excels in exploitation. To address this limitation, incorporating initial values leads to improved performance and a better match with the real experimental observations. Experimentation was conducted to find the best model parameters for the desired suction pressure. The therapy can be administered with greater precision and efficacy by accurately identifying the suction pressure. Overall, this research represents a promising development in cupping therapy. In particular, it has been demonstrated that the proposed nonlinear Hammerstein models improve accuracy by 84.34% through the tuning SED algorithm