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Abstract A comprehensive artificial intelligence-based motor drive was developed to control the performance of a permanent magnet direct current (PMDC) motor employed as a small-scale three-bladed horizontal axis ocean current turbine. Although the conventional controller performs reasonably in a lab environment where non-linear load is absented; however, for towing tank experiments with noisy and potentially non-linear input, it is crucial to run the small-scale turbine in a robust mode. A mathematical model of a PMDC motor dynamic system is derived incorporating a fuzzy logic controller. In addition, this drive control was validated experimentally. The experimental design is discussed in detail. The system performance was tested experimentally over a wide range of operating condition to validate the fuzzy logic control robustness and effectiveness. Also, it is shown that the speed of the PMDC motor was controlled by using this fuzzy logic controller. The speed tracking shows good agreement with the reference speed regardless of the load condition. 

Abstract A comprehensive numerical model was developed to address the performance of a permanent magnet direct current (PMDC) motor which is employed as a small-scale three-bladed horizontal axis ocean current turbine. This numerical model development is presented along with a comparison to experimental data to quantify the motor performance. The proposed experimental design is discussed in detail. Due to the nature of the ocean current turbine, it is required to run it first by applying input power, subsequently to be governed by hydrokinetic energy. Thus, a detailed performance of the PMDC motor is essential when it runs as a motor and generator. Based on our preliminary work, the angular speed of the small-scale turbine is less than 500 rpm. Thus, a combination of the PMDC motor and a planetary gearhead is used to fulfill this low-speed requirement. The gearhead is driven in reverse when operating as a generator which leads to poor efficiency. This efficiency is experimentally derived to be 47.8% at maximum speed of 479.4 rpm at 12V. 

The hydrodynamic forces on an oscillating circular cylinder are predicted using neural networks under flow conditions where Vortex-Induced Vibrations (VIV) are known to occur. The derived neural network approximators are then incorporated in a dynamical model that allows prediction of the cylinder motion given flow conditions and initial conditions. Using experimental data, a minimum-least-squares compensator is tuned that includes linear stiffness and damping su-perimposed with a constant force offset. The compensator is decoupled, i.e., with equations in-dependent for each degree of freedom. By applying the neural network approximators and the derived compensator simulated experiments can be performed. These simulated experiments show that the compensator which cancels the linear components and any bias in the hydrody-namic forces effectively stabilizes the VIV motion. To support this time-domain analysis is per-formed along with phase-plane investigations. Maximum Lyapunov exponent analysis is also shown. 

A linear mechanical oscillator is non-linearly coupled with an electromagnet and its driving circuit through a magnetic field. The resulting non-linear dynamics are investigated using magnetic circuit approximations without major loss of accuracy and in the interest of brevity. Different computational approaches to simulate the setup in terms of dynamical system response and design parameters optimization are pursued. A current source operating in baseband without modulation directly feeds the electromagnet, which consists commonly of a solenoid and a horseshoe-shaped core. The electromagnet is then magnetically coupled to a mass made of soft magnetic material and attached to a spring with damping. The non-linear system is described by a linearized steady-space representation while is examined for controllability and observability. A controller using a pole placement approach is built to stabilize the element. Drawing upon the fact that coupling works both ways, enabling estimation of the mass position and velocity (state variables) by processing the induced voltage across the electromagnet, a state observer is constructed. Accurate and fast tracking of the state variables, along with the possibility of driving more than one module from the same source using modulation, proves the applicability of the electro-magneto-mechanical transducer for sensor applications. Next, a three-layer feed-forward artificial neural network (ANN) system equivalent was trained using the non-linear plant-linear controller-linear observer configuration. Simulations to investigate the robustness of the system with respect to different equilibrium points and input currents were carried out. The ANN proved robust with respect to position accuracy.