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

Performance Comparisan Between Sliding Mode Control (Smc) And Pd-Pid Controllers For A Nonlinear Inverted Pendulum System

The objective of this paper is to compare the time specification performance between conventional controller PID and modern controller SMC for an inverted pendulum system. The goal is to determine which control strategy delivers better performance with respect to pendulum's angle and cart's position. The inverted pendulum represents a challenging control problem, which continually moves toward an uncontrolled state. Two controllers are presented such as Sliding Mode Control (SMC) and Proportional- Integral-Derivatives (PID) controllers for controlling the highly nonlinear system of inverted pendulum model. Simulation study has been done in Matlab Mfile and simulink environment shows that both controllers are capable to control multi output inverted pendulum system successfully. The result shows that Sliding Mode Control (SMC) produced better response compared to PID control strategies and the responses are presented in time domain with the details analysis

Comparative Assessment of Feed-forward Schemes with NCTF for Sway and Trajectory Control of a DPTOC

This paper presents a comparative assessment of feed-forward schemes in hybrid control schemes for anti-swaying and trajectory tracking of a double-pendulum-type overhead crane (DPTOC) system. A nonlinear DPTOC system is considered and the dynamic model of the system is derived using the Euler-Lagrange formulation. To study the effectiveness of the controllers, initially nominal characteristics following trajectory following (NCTF) is developed for position control of cart movement. The controller design, which is comprised of a nominal characteristic trajectory (NCT) and PI compensator, is used to make the cart motion follow the NCT. This is then extended to incorporate feed-forward schemes for anti-swaying control of the system. Feed-forward control schemes based on input shaper and filtering techniques are to be examined. The input shaper and filtering techniques with different orders were designed based on properties of the system. The results of the response with the controllers are presented in time and frequency domains. The performances of hybrid control schemes are examined in terms of level of input tracking capability, sway angle reduction and time response specifications in comparison to NCTF controller. Finally, a comparative assessment of the control techniques is discussed and presented

Model-free controller design based on simultaneous perturbation stochastic approximation

Recently, with the rapid growth in science and engineering, most of the real world process plants have been built on a large scale and complex systems. As a consequence, modeling of such systems may become very difficult and require a lot of effort. Therefore, it is necessary to develop a control method that does not depend on plant models, which is known as the model-free control approach. At the same time, it is also worthy to consider an optimization tool for the model-free approach that is simple to understand for engineers and can optimize a large number of control parameters in a fast manner. So far, there have not been enough literatures to discuss the application of model-free control schemes for the above demands. Motivated by the above background, a model-free control scheme is considered in our study. Here, a simultaneous perturbation stochastic approximation (SPSA) algorithm is suggested as a promising tool for the model-free control approach. Then, this dissertation focuses on assessing the effectiveness of the SPSA-based algorithm for various modelfree control problems such as P11) tuning of MIMO systems, optimizing fuel consumption of hybrid electric vehicles, and maximizing power production of wind farms. Firstly, we present a performance comparison of SPSA-based methods for P11) tuning of MllvIO systems. In particular, four typical SPSA-based methods, which are onemeasurement SPSA (1SPSA), two-measurement SPSA (2SPSA), global SPSA (GSPSA), and adaptive SPSA (ASPSA) are examined. Their performances are evaluated through extensive simulation for several controller design examples, in terms of stability of the closed-loop systems, tracking performance, and computation time. In addition, the performance of the SPSA-based methods is compared to the other stochastic optimization based approaches. Secondly, we propose a model-free controller design for hybrid electric vehicle systems. Here, a switching control scheme is adopted, where each sub-controller is specified for each driving condition, in order to improve the fuel efficiency. An SPSA-based method is utilized to optimize a large number of design parameters in the switching controller. The design method is applied to the JSAE-SICE benchmark problem, which is developed using GT-SUITE of Gamma Technologies, Inc. and integrated with Simulink / MATLAB. The effectiveness of the proposed controller is evaluated in terms of the fuel efficiency improvement and driver's satisfaction, as compared to the sample controller of the benchmark problem. Finally, we provide a model-free approach for maximizing power production of wind farms. Based on the information on the wind farm configuration, such as the turbine location and wind direction, we propose a multi-resolution SPSA (MRSPSA)-based method that can achieve fast model-free controller tuning. In order to evaluate the effectiveness of our proposed scheme, a wind farm model with dynamic characterization of wake interaction between turbines is used and then the proposed method is applied to the Horns Rev wind farm. Furthermore, the performance of the MR-SPSA-based method is also compared with other existing model-free methods, in terms of maximum power production and convergence time

Identification of the thermoelectric cooler using hybrid multi-verse optimizer and sine cosine algorithm based continuous-time Hammerstein model

This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. These modifications are mainly for escaping from local minima and for making the balance between exploration and exploitation. In the Hammerstein model identification a continuoustime linear system is used and the hMVOSCA based method is used to tune the coefficients of both the Hammerstein model subsystems (linear and nonlinear) such that the error between the estimated output and the actual output is reduced. The efficiency of the proposed method is evaluated based on the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon’s rank test. The experimental findings show that the hMVOSCA can produce a Hammerstein system that generates an estimated output like the actual TEC output. Moreover, the identified outputs also show that the hMVOSCA outperforms other popular metaheuristic algorithms

Unilateral Renal Tuberculosis Presenting as Persistent Pyuria

Despite being one of the major health problem globally, tuberculosis still remains an important, but under diagnosed and ignored cause of kidney damage especially in resource poor settings. Timely diagnoses and treatment can cure this otherwise devastating resource draining ailment. We report a 10-year-old girl who had persistent pyuria and dysuria despite receiving multiple drugs empirically before antitubercular therapy was initiated. Keywords: Child; Renal Tuberculosis; Urinary Tract infections

Computer-Based System For Calibration Of Temperature Transmitter Using Rtd

Using the temperature calibration instrument, the purpose of this paper is to design the uncertainty calculation system. The test was conducted using Resistance Temperature Detector (RTD) sensor, transmitter, and repeated for three times. The data acquisition (DAQ) card is used to interface the temperature instrument and the computer. In order to determine the uncertainty of the temperature measurement, graphical user interface (GUI) software has been developed in Visual Basic(VB) programming language. The developed software shows that the uncertainty of the temperature transmitter measurement can be calculated by interfacing the instrument to the computer through DAQ card. The study focuses on manual temperature measurement and concentrates only on RTD temperature sensor. The results provide the confidence limits of five-point calibration of temperature transmitter that could improve the teaching techniques using computer-based system of the temperature measurement

Optimal tuning of sigmoid PID controller using nonlinear sine cosine algorithm for the automatic voltage regulator system --- KIV (status in press)

Automatic Voltage Regulator (AVR) is fabricated to sustain the voltage level of a synchronous generator spontaneously. Several control strategies have been introduced into the AVR system with the aim of gaining a better dynamic response. One of the most universally utilized controllers is the Proportional-Integral-Derivative (PID) controller. Despite the PID controller having a relatively high dynamic response, there are still further possibilities to improve in order to obtain more appropriate responses. This paper designed a sigmoid-based PID (SPID) controller for the AVR system in order to allow for an accelerated settling to rated voltage, as well as increasing the control accuracy. In addition, the parameters of the proposed SPID controller are obtained using an enhanced self-tuning heuristic optimization method called Nonlinear Sine Cosine Algorithm (NSCA), for achieving a better dynamic response, particularly with regards to the steady-state errors and overshoot of the system. A time-response specifications index is used to validate the proposed SPID controller. The obtained simulation results revealed that the proposed method was not only highly effective but also greatly improved the AVR system transient response in comparison to those with the modern heuristic optimization based PID controllers

Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm

This paper presents a preliminary study of a model-free approach based on spiral dynamic algorithm (SDA) for maximizing wind farms power production. The SDA based approach is utilized to find the optimal control parameter of each turbine to maximize the total power production of a wind farm. For simplicity, a single row wind farm model with turbulence interaction between turbines is used to validate the proposed approach. Simulation results demonstrate that the SDA based method produces higher total power production compared to the particle swarm optimization (PSO) and game theoretic (GT) based approaches

A data-driven neuroendocrine-PID controller for underactuated systems based on safe experimentation dynamics

This paper presents a data-driven neuroendocrine-PID controller for underactuated systems. Safe Experimentation Dynamics (SED) is employed to find the optimum neuroendocrine-PID parameters such that the control tracking performance and input energy are minimized. The advantage of the proposed approach is that it can generate fast neuroendocrine-PID parameter tuning by measuring the input and output data of the system without using the plant mathematical model. Moreover, the combination of neuroendocrine structure with PID has a great potential in improving the control performance as compared to the PID controller. An underactuated container crane model is considered to validate the proposed data-driven design. In addition, the performance of the proposed method is investigated in terms of the trolley position, hoist rope length and sway angle trajectory tracking. The simulation results show that the data-driven neuroendocrine-PID approach provides better control performance as compared to the PID controller