System identification and pid control of toothbrush simulator system

Toothbrush simulator was invented for industry and dentist researchers to do research related to plaque removal. The toothbrush simulator system repeatedly has a problem in achieving the desired speed control. The brushing movement is inconsistence and stops eventually if there is a force exerted on the toothbrush holder. Further research is required to increase the reliability and controllability of the speed response achievable from the toothbrush simulator system. In this study, a PID controller is designed and embedded in the system. A real-time experiment has been conducted on the real system via the Matlab Simulink environment to construct the model. The model parameters are optimized with model order 2, 3 and 4 where each model order has been analyzed for ten (10) times iteration by the genetic algorithm in obtaining the accurate transfer function. The model has been validated through correlation analysis. The PID controller was tuned through the PID tuner and Ziegler-Nichols method. Simulated and real-time system response from both tuning methods was compared. The simulated response with the selected PID controller is then compared with the response from the real-time experiment. The closed-loop system without controller was compared with the response with the PID controller. The PID controller was then deployed into the real system by uploaded into the microcontroller. The brushing simulator remote control was created to control the desired speed through a smartphone. Genetic algorithm model based on model order 4 has been selected as the best model as it able to achieve the minimum MSE value of 0.0176 and past all the validation tests. The selected PID parameters was from PID tuner tuning method with gain values of; Kp= 17.9287, Ki= 40.751 and Kd= -0.52705. Both results of simulation and real-time tests were compared, and they show about similar performances. The controlled system response had achieved all five desired speed of 175, 195, 215, 235 and 255 rpm with the percentage of improvement 67%, 65%, 65%, 65%, and 68%. Throughout this study, a genetic algorithm model based and tuned PID controller parameters has been applied to the real system improvised in better system response

Neural network algorithm-based fall detection modelling

Fall is a major threat among elderly people which may lead to injuries or even death. High recognition of developed fall detection model is very significance for the elderly to detect the falls. Related algorithm for the fall detection has been discussed in depth by researcher from the previous research. However, the improvement of model accuracy is still needed. This article presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet algorithm. The algorithm is trained by network training function; LM, SCG and RP by collocation with threshold-based setting value. Two participants involved in obtaining the acceleration and angular velocity. The type of input source is divided into 4 different types for training. The selection of the model was based on the comparison of optimization epochs, magnitude of validate error or mean square error (MSE), magnitude of correlation performance, the convergence graph in term of MSE performance, accuracy of regression and non-zero value of autocorrelation graph. The simulated result shows that the training model of Type 2 is the best model with a training result of 6.1551mse, 40 epochs, time 0.06s, and 0.92742 accuracy. The result indicates that LM function produce the better solution when compared to another optimization function. In fact, the model accuracy demonstrated that the proposed method was reliable and efficient

Experimental evaluation of fish feeder machine controller system

The aim of this project is to apply the automatic fish feeder controller system for fish pellet dispersion machine. The system uses Arduino as a microcontroller which programmed by setting the speed and timer of each DC motor to make this machine functioning automatically. When the adapter is switch, the microcontroller will be connected then activate the motor driver to generate the system. Two motors have been used as motor 1 and motor 2 which functions to drop and disperse the fish pellet respectively. The whole system was programmed through a specific coding and uploaded to the Arduino. This project benefits in reduce the manpower for the fish farming. The component that required to build this project is portable and cost-effective. From experimental result, the timer for motor 1 to dispense the fish food from the storage was 60s while 90s for DC motor 2 as a distributor. The speed for motor 1 was set to 110rpm while motor 2 was set with full speed of 255rpm. The fish pellet can be distributed and scattered up to 5m which can reduce the competition among the fish at one place

Neural Network Algorithm-based Fall Detection Modelling

Falling is a well-known as a threat among elderly people which may lead to injuries or even death. High recognition of developed fall detection model is very significance for the elderly to detect the falls. Related algorithm for the fall detection has been discussed in depth by researcher during the past decade. However, the improvement of model accuracy is still needed. This article presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet algorithm. The algorithm is trained by network training function; LM, SCG and RP by collocation with threshold-based setting value. Two participants involved in obtaining the acceleration and angular velocity. The training is divided into 4 different type of input source. The selection of the model was based on the comparison of optimization epochs, magnitude of validate error or mean square error (MSE), magnitude of correlation performance, the progress of convergence graph in term of MSE performance, accuracy of regression and non-zero value in autocorrelation graph. The simulated result shows that the training model of Type 2 is the best model with a training result of 6.1551mse, 40 epochs, time 0.06s, and 0.92742 accuracy. The result indicates that LM function produce the better solution when compared to another optimization function. In fact, the model accuracy demonstrated that the proposed method was reliable and efficient

Neural Network Algorithm-based Fall Detection Modelling

Falling is a well-known as a threat among elderly people which may lead to injuries or even death. High recognition of developed fall detection model is very significance for the elderly to detect the falls. Related algorithm for the fall detection has been discussed in depth by researcher during the past decade. However, the improvement of model accuracy is still needed. This article presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet algorithm. The algorithm is trained by network training function; LM, SCG and RP by collocation with threshold-based setting value. Two participants involved in obtaining the acceleration and angular velocity. The training is divided into 4 different type of input source. The selection of the model was based on the comparison of optimization epochs, magnitude of validate error or mean square error (MSE), magnitude of correlation performance, the progress of convergence graph in term of MSE performance, accuracy of regression and non-zero value in autocorrelation graph. The simulated result shows that the training model of Type 2 is the best model with a training result of 6.1551mse, 40 epochs, time 0.06s, and 0.92742 accuracy. The result indicates that LM function produce the better solution when compared to another optimization function. In fact, the model accuracy demonstrated that the proposed method was reliable and efficient

Experimental Evaluation of Fish Feeder Machine Controller System

The aim of this project is to apply the automatic fish feeder controller system for fish pellet dispersion machine. The system uses Arduino as a microcontroller which programmed by setting the speed and timer of each DC motor to make this machine functioning automatically. When the adapter is switch, the microcontroller will be connected then activate the motor driver to generate the system. Two motors have been used as motor 1 and motor 2 which functions to drop and disperse the fish pellet respectively. The whole system was programmed through a specific coding and uploaded to the Arduino. This project benefits in reduce the manpower for the fish farming. The component that required to build this project is portable and cost-effective. From experimental result, the timer for motor 1 to dispense the fish food from the storage was 60s while 90s for DC motor 2 as a distributor. The speed for motor 1 was set to 110rpm while motor 2 was set with full speed of 255rpm. The fish pellet can be distributed and scattered up to 5m which can reduce the competition among the fish at one place