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Nowadays, various types of vehicle motion control systems are common in the automotive industry. However, the request for more advanced and performant control systems is very strong, expecially in the two fields of high-performance passenger vehicles and self-driving vehicles. For example, feedback control is applied in sports cars to fully exploit the potential of the vehicle and enhance the fun-to-drive element for the driver, while also improving safety. Similarly, a number of challenges are connected to the increasing popularity of the autonomous cars, requiring novel solutions for vehicle dynamics control.
Within this context, this work is focused on the development of advanced control techniques, including strongly model-based control techniques such as Model Predictive Control, and real-time optimization in general. The application of optimization-based algorithms is explored for both passenger and autonomous cars, focusing the research activity on the main challenges that arise in the context of lateral dynamics control for high-performance vehicles.