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This paper proposes a new artificial neural network-based position controller for a full-electric injection moulding machine. Such a controller improves the dynamic characteristics of the positioning for hot runners, pin valve and the injection motors for varying moulding parameters. Practical experimental data and Matlab’s System Identification Toolbox have been used to identify the transfer functions of the motors. The structure of the artificial neural network, which used positioning error and speed of error, was obtained by numerical modelling in Matlab/Simulink. The artificial neural network was trained using back-propagation algorithms to provide control of the motor current thus ensuring the required position and velocity. The efficiency of the proposed ANN-based controller has been estimated and verified in Simulink using real velocity data and the position of the injection moulding machine and pin valve motors.
This paper discusses an approach of the abnormal condition detection of whole blood using piezo-synthetic effects in blood under dynamic external pressure. Three groups of samples having verified chemical and biological conditions were analysed to prove reliable detection: saline, whole blood and whole blood with colorectal cancer as an example of abnormal conditions. The procedure of a discrete differentiation process for obtained experimental data has been proposed as preliminary processing. Three information parameters have been selected to describe experimental data. Fischer F-statistics were used to determine the information content of the proposed information parameters. It has been proved that the proposed information parameters react on changing state of object under test and therefore can be effectively used for the abnormal condition detection.
This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.
This paper provides a performance analysis of a wearable photovoltaic system mounted on the outer surface of a backpack. Three types of photovoltaic materials, commonly used for electricity generation, have been investigated under various conditions including sun irradiance, angle-of-incidence and sun inclination. The results of the investigation have shown that the system equipped with the rigid mono-Si panels performs 3.5 to 4.9 times better than the system equipped with a-Si flexible PV modules. The average power generated by the wearable photovoltaic system is about 30% of the maximum installed power for any photovoltaic type. This paper presents the test data resulting from the evaluation of the daily energy production of a wearable photovoltaic power supply.
This paper proposes an approach to an ANN-based temperature controller design for a plastic injection moulding system. This design approach is applied to the development of a controller based on a combination of a classical ANN and integrator. The controller provides a fast temperature response and zero steady-state error for three typical heaters (bar, nozzle, and cartridge) for a plastic moulding system. The simulation results in Matlab Simulink software and in comparison to an industrial PID regulator have shown the advantages of the controller, such as significantly less overshoot and faster transient (compared to PID with autotuning) for all examined heaters. In order to verify the proposed approach, the designed ANN controller was implemented and tested using an experimental setup based on an STM32 board.
