Analysis and Implementation of Multiphase-Multilevel Inverter for Open-Winding Loads

Research work carried out in focusing a novel multiphase-multilevel ac motor drive system much suitable for low-voltage high-current power applications. In specific, six-phase asymmetrical induction motor with open-end stator winding configuration, fed from four standard two-level three-phase voltage source inverters (VSIs). Proposed synchronous reference frame control algorithm shares the total dc source power among the 4 VSIs in each switching cycle with three degree of freedom. Precisely, first degree of freedom concerns with the current sharing between two three-phase stator windings. Based on modified multilevel space vector pulse width modulation shares the voltage between each single VSIs of two three-phase stator windings with second and third degree of freedom, having proper multilevel output waveforms. Complete model of whole ac motor drive based on three-phase space vector decomposition approach was developed in PLECS - numerical simulation software working in MATLAB environment. Proposed synchronous reference control algorithm was framed in MATLAB with modified multilevel space vector pulse width modulator. The effectiveness of the entire ac motor drives system was tested. Simulation results are given in detail to show symmetrical and asymmetrical, power sharing conditions. Furthermore, the three degree of freedom are exploited to investigate fault tolerant capabilities in post-fault conditions. Complete set of simulation results are provided when one, two and three VSIs are faulty. Hardware prototype model of quad-inverter was implemented with two passive three-phase open-winding loads using two TMS320F2812 DSP controllers. Developed McBSP (multi-channel buffered serial port) communication algorithm able to control the four VSIs for PWM communication and synchronization. Open-loop control scheme based on inverse three-phase decomposition approach was developed to control entire quad-inverter configuration and tested with balanced and unbalanced operating conditions with simplified PWM techniques. Both simulation and experimental results are always in good agreement with theoretical developments.

Subject-specific musculoskeletal models of the lower limbs for the prediction of skeletal loads during motion

The determination of skeletal loading conditions in vivo and their relationship to the health of bone tissues, remain an open question. Computational modeling of the musculoskeletal system is the only practicable method providing a valuable approach to muscle and joint loading analyses, although crucial shortcomings limit the translation process of computational methods into the orthopedic and neurological practice. A growing attention focused on subject-specific modeling, particularly when pathological musculoskeletal conditions need to be studied. Nevertheless, subject-specific data cannot be always collected in the research and clinical practice, and there is a lack of efficient methods and frameworks for building models and incorporating them in simulations of motion. The overall aim of the present PhD thesis was to introduce improvements to the state-of-the-art musculoskeletal modeling for the prediction of physiological muscle and joint loads during motion. A threefold goal was articulated as follows: (i) develop state-of-the art subject-specific models and analyze skeletal load predictions; (ii) analyze the sensitivity of model predictions to relevant musculotendon model parameters and kinematic uncertainties; (iii) design an efficient software framework simplifying the effort-intensive phases of subject-specific modeling pre-processing. The first goal underlined the relevance of subject-specific musculoskeletal modeling to determine physiological skeletal loads during gait, corroborating the choice of full subject-specific modeling for the analyses of pathological conditions. The second goal characterized the sensitivity of skeletal load predictions to major musculotendon parameters and kinematic uncertainties, and robust probabilistic methods were applied for methodological and clinical purposes. The last goal created an efficient software framework for subject-specific modeling and simulation, which is practical, user friendly and effort effective. Future research development aims at the implementation of more accurate models describing lower-limb joint mechanics and musculotendon paths, and the assessment of an overall scenario of the crucial model parameters affecting the skeletal load predictions through probabilistic modeling.