Design and implementation of motorbike lean angle observers

This thesis focuses on the investigation and the implementation of different observers for the estimation of the roll angle of a motorbike. The central core of the activity is applying a Model-Based design in order to outline, simulate and implement the filters with the aim of a final comparison of the performances. This approach is crucially underlined among the chapters that articulate this document: first the design and tuning of an Extended Kalman Filter and a Complementary Filter in a pure simulation environment emphasize the most accurate choice for the particular problem. After this, several steps were performed in order to move from the aforementioned simulation environment to a real hardware application. In conclusion, several sensor configurations were tested and compared in order to highlight which sensor suite gives the best performances.

Power consumption analysis for self-diagnosis of Wireless Sensor Nodes

Wireless sensor networks can transform our buildings in smart environments, improving comfort, energy efficiency and safety. Today however, wireless sensor networks are not considered reliable enough for being deployed on large scale. In this thesis, we study the main failure causes for wireless sensor networks, the existing solutions to improve reliability and investigate the possibility to implement self-diagnosis through power consumption measurements on the sensor nodes. Especially, we focus our interest on faults that generate in-range errors: those are wrong readings but belong to the range of the sensor and can therefore be missed by external observers. Using a wireless sensor network deployed in the R\&D building of NXP at the High Tech Campus of Eindhoven, we performed a power consumption characterization of the Wireless Autonomous Sensor (WAS), and studied through some experiments the effect that faults have in the power consumption of the sensor.

Catch and by-catch analyses of tropical tuna fishery in the Gulf of Guinea from 2010 to 2012.

Canned tuna is one of the most widespread and recognizable fish commodities in the world. Over all oceans 80% of the total tuna catches are caught by purse seine fishery and in tropical waters their target species are: yellowfin (Thunnus albacares), bigeye (Thunnus obesus) and skipjack (Katsuwonus pelamis). Even if this fishing gear is claimed to be very selective, there are high levels of by-catch especially when operating under Fish Aggregating Devices (FADs). The main problem is underestimation of by-catch data. In order to solve this problem the scientific community has developed many specific programs (e.g. Observe Program) to collect data about both target species and by-catch with observers onboard. The purposes of this study are to estimate the quantity and composition of target species and by-catch by tuna purse seiner fishery operating in tropical waters and to underline a possible seasonal variability in the by-catch ratio (tunas versus by-catch). Data were collected with the French scientific program ”Observe” on board of the French tuna purse seiner “Via Avenir” during a fishing trip in the Gulf of Guinea (C-E Atlantic) from August to September 2012. Furthermore some by-catch specimens have been sampled to obtain more information about size class composition. In order to achieve those purposes we have shared our data with the French Institute of Research for the Development (IRD), which has data collected by observers onboard in the same study area. Yellowfin tuna results to be the main specie caught in all trips considered (around 71% of the total catches) especially on free swimming schools (FSC) sets. Instead skipjack tuna is the main specie caught under FADs. Different percentages of by-catch with the two fishing modes are observed: the by-catch incidence is higher on FADs sets (96.5% of total by-catch) than on FSC sets (3.5%) and the main category of by-catch is little-tuna (73%). When pooling data for both fishing sets used in purse seine fishery the overall by-catch/catch ratio is 5%, a lower level than in other fishing gears like long-lining and trawling.

Application of the Extended Kalman filter for speed sensorless control of PM synchronous machines

The increasing interest in the decarbonization process led to a rapidly growing trend of electrification strategies in the automotive industry. In particular, OEMs are pushing towards the development and production of efficient electric vehicles. Moreover, research on electric motors and their control are exploding in popularity. The increase of computational power in embedded control hardware is allowing the development of new control algorithm, such as sensorless control strategy. Such control strategy allows the reduction of the number of sensors, which implies reduced costs and increased system reliability. The thesis objective is to realize a sensorless control for high-performance automotive motors. Several algorithms for rotor angle observers are implemented in the MATLAB and Simulink environment, with emphasis on the Kalman observer. One of the Kalman algorithms already available in the literature has been selected, implemented and benchmarked, with emphasis on its comparison with the Sliding Mode observer. Different models characterized by increasing levels of complexity are simulated. A simplified synchronous motor with ”constant parameters”, controlled by an ideal inverter is first analyzed; followed by a complete model defined by real motor maps, and controlled by a switching inverter. Finally, it was possible to test the developed algorithm on a real electric motor mounted on a test bench. A wide range of different electric motors have been simulated, which led to an exhaustive review of the sensorless control algorithm. The final results underline the capability of the Kalman observer to effectively control the motor on a real test bench.