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The paper presents a new control strategy of management of transport companies operating in completive transport environment. It is aimed to optimise the headway of transport companies to provide the balance between costs and benefits of operation under competition. The model of transport system build using AnyLogic comprises agent-based and discrete-event techniques. The model combined two transport companies was investigated under condition of the competition between them. It was demonstrated that the control strategy can ensure the balance of interests of transport companies trying to find compromise between cost of operation and quality of service.
Synthesis of serving policies for objects flow in the system with refillable storage component
(2017)
The article presents a solution to detect rotor position at stand still condition for all types of permanent magnet brushless dc motors. The solution provides both secure and fast method for starting of the brushless motor, that is independent on the sensorless control scheme used. Nonlinearities found in standard three phase permanent magnet dc motor are used to derive the rotor position at stand still. The described solution assumes that there is availability of the neutral point of the three phase star motor windings.
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.
Error analysis in a high accuracy sampled-data velocity stabilising system using Volterra series
(2015)
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.
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.