Browsing by Author "Economou, John"

Now showing 1 - 4 of 4
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    A two-stage classification method for improved positioning using low-cost inertial sensors
    (IEEE, 2024-08-08) Maton, Dariusz; Economou, John; Galvao Wall, David; Khan, Irfan; Cooper, Robert; Ward, David; Trythall, Simon
    The two-stage subtractive clustering Takagi-Sugeno (2SC-TS) method is proposed which completely replaces the established method of inertial navigation with classification models. The classifiers are designed by the subtractive clustering algorithm, an unsupervised learning method. The accuracy of the trajectories is compared against three competitive data-driven methods on three independent experimental datasets. The results show how 2SC-TS generates trajectories with approximately 20% lower positional error compared with the single-stage version (SC-TS), and halves the error produced by competitive deep learning methods. The proposed method may help improve the positioning of people and robots carrying low-cost inertial sensors.
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    Investigation and analysis of two-layer spoke-type ferrite interior permanent magnet machine
    (Cranfield University, 2019-07) Abdurahem, Hayder Abdulhasan; Luk, Patrick Chi-Kwong; Economou, John
    A Novel two-layer spoke-type ferrite IPM design is presented based on a commercial induction motor as a low-cost high-performance alternative for new installation to meet efficiency requirements “greener” for various variable speed applications. The proposed design improves PM flux by flux-focusing techniques and maximises the reluctance torque with a two-layer structure. High torque density and efficiency are achieved with comparable performance to a rare-earth equivalent. At first, two-Dimensional d-q frame analytical methods are developed based on the magnetic circuit models. Then, FEA models of the ferrite and induction motors are built and analysed. A comprehensive investigation is carried out focusing on the performances in terms of torque profiles, losses, efficiency and power factor. The influences of temperature variation and distribution on the performance of novel two-layer spoke-type interior permanent magnet machines under different operating conditions are analysed. The analysis is based on a developed thermal circuit model. For benchmarking, a standard industrial IM is used. “Comprehensive experimental tests are undertaken, and the results show the proposed ferrite IPM machine has distinctive advantages over the IM in terms of efficiency, power density and speed range. In addition, due to low cost ferrite materials, it has a comparable price advantage. Finally, A prototype machine with the proposed design is manufactured, and both the two motors are tested under various operating conditions. The experimental results confirm the FEA simulations and validate superior performance of the proposed ferrite motor over its induction counterpart.
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    Low-cost IMU Sensor Temperature Humidity Zero Bias Data
    (Cranfield University, 2024-05-13 11:08) Maton, Dariusz; Economou, John; Galvao Wall, David; Khan, Irfan; Cooper, Rob
    Dataset containing the responses of three inertial measurement units (IMUs) of the same model (MPU-6050s) under varying temperature and relative humidity conditions in a Sanyo Gallenkamp environmental chamber.
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    Tuning of a Complementary Orientation Filter Using Velocity Data and a Genetic Algorithm
    (Cranfield University, 2024-01-08 14:59) Maton, Dariusz; Economou, John; Khan, Irfan; Galvao Wall, David; Cooper, Rob
    The data uploaded here contains the experimental and simulation data used to demonstrate the utility of the optimisation of a complementary orientation filter using a genetic algorithm (GA). Implementation of the GA in MATLAB is provided as well as supporting functions such as the zero velocity update and weighted-relative velocity error metric (W-RVE). The novelty of the work is the optimal tuning of the complementary filter gain using a GA and velocity data of an object moving in the locally level frame. Optimal filter gains are encoded a Takagi-Sugeno (TS) fuzzy inference system with four Gaussian membership functions. This offers a transparent and traceable encoding.