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The introduction of new steering conceptsSteer-by-Wire (SBW) gives possibility to replace theconventional steering wheel by an alternative userinterface such as a sidestick. In SBW system the sidestickcan be used as user input element instead of a steeringwheel. The implementation of sidestick in the Human-Machine-Interface (HMI) allows combiningthe conventional steering consisting of a steeringwheel, an accelerator and a brake pedal into a singleelement. Also the implementation of the sidestickcreates new, interesting and flexible design optionswhich can be used to transform the driver’s spatialenvironment. This article describes an active sidestickfor a vehicle which has been developed, integrated andtested in accordance of haptic, ergonomic and safetyrelevant requirements. The control strategies used forthe active attenuators of the sidestick have beeninvestigated and optimised using a Simulink model.
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.
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.
The small and remote households in Northern regions demand thermal energy rather than electricity. Wind turbine in such places can be used to convert wind energy into thermal energy directly using a heat generator based on the principle of the Joule machine. The heat generator driven by a wind turbine can reduce the cost of energy for heating system. However the optimal performance of the system depends on the torque-speed characteristics of the wind turbine and the heat generator. To achieve maximum efficiency of operation both characteristics should be matched. In the article the condition of optimal performance is developed and an example of the system operating at maximum efficiency is simulated.
Error analysis in a high accuracy sampled-data velocity stabilising system using Volterra series
(2015)
Power train models are required to simulate hence predict energy consumption of vehicles. Efficiencies for different components in power train are required. Common procedures use digitalised shell models (or maps) to model the efficiency of Internal Combustion Engines (ICE) and manual gearboxes (MG). Errors are connected with these models and affect the accuracy of the calculation. The accuracy depends on the configuration of the simulation, the digitalisation of the data and the data used. This paper evaluates these sources of error. The understanding of the source of error can improve the results of the modelling by more than eight percent.