Development of a vehicle dynamics controller for obstacle avoidance

As roads become busier and automotive technology improves, there is considerable potential for driver assistance systems to improve the safety of road users. Longitudinal collision warning and collision avoidance systems are starting to appear on production cars to assist drivers when required to stop in an emergency. Many luxury cars are also equipped with stability augmentation systems that prevent the car from spinning out of control during aggressive lateral manoeuvres. Combining these concepts, there is a natural progression to systems that could assist in aiding or performing lateral collision avoidance manoeuvres. A successful automatic lateral collision avoidance system would require convergent development of many fields of technology, from sensors and instrumentation to aid environmental awareness through to improvements in driver vehicle interfaces so that a degree of control can be smoothly and safely transferred between the driver and vehicle computer. A fundamental requirement of any collision avoidance system is determination of a feasible path that avoids obstacles and a means of causing the vehicle to follow that trajectory. This research focuses on feasible trajectory generation and development of an automatic obstacle avoidance controller that integrates steering and braking action. A controller is developed to cause a specially modified car (a Mercedes `S' class with steer-by-wire and brake-by-wire capability) to perform an ISO 3888-2 emergency obstacle avoidance manoeuvre. A nonlinear two-track vehicle model is developed and used to derive optimal controller parameters using a series of simulations. Feedforward and feedback control is used to track a feasible reference trajectory. The feedforward control loops use inverse models of the vehicle dynamics. The feedback control loops are implemented as linear proportional controllers with a force allocation matrix used to apportion braking effort between redundant actuators. Two trajectory generation routines are developed: a geometric method, for steering a vehicle at its physical limits; and an optimal method, which integrates steering and braking action to make full use of available traction. The optimal trajectory is obtained using a multi-stage convex optimisation procedure. The overall controller performance is validated by simulation using a complex proprietary model of the vehicle that is reported to have been validated and calibrated against experimental data over several years of use in an industrial environment

Numerical model estimation of biomethane production using an anaerobic CSTR: model formulation, parameter estimation and uncertainty/sensitivity analysis

Global climate change is becoming of increasing concern. Transportation makes up a large part of carbon gasses, which affects climate change and air quality. As transportation is a big part of carbon emissions, everybody can contribute to reducing emissions through transportation. One way for people to contribute is to start choosing greener transportation. Nuding is a tool that can be used to get people to choose greener transportation. It's function is to help guide people's behavior. For this project, the nudging goal is to nudge people towards healthier and greener transportation options than already in use. An example of a nudge is to provide reminders of bus departure times for a trip to an event. In order to nudge people gathering information relevant for traveling is necessary. In this thesis, relevant information for green transportation nudges is researched. Other studies on green transportation nudges are applied to discover relevant information topics and sources. Microservices architecture is proposed as the architecture for designing nudges, where the system is divided into smaller interconnected services that work together. Demonstrators of information collection microservices are designed and implemented. The demonstrators handle data for different information topics relevant to green transportation nudges. There are demonstrators for collecting weather data, routing data, public transportation data, rental bikes and scooters data, calendar data, and location data. The thesis also discusses how the data collected can be used to form transportation nudges

Experimental set-up for investigation of fault diagnosis of a centrifugal pump

Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated