Data Driven Control For Power Converters

Nowadays power converters are essential devices in modern electrical and electronic systems, serving as intermediaries that modify electrical energy from one form to another to suit various applications. The fundamental role of power converters is to efficiently manage and control the flow of electrical energy, ensuring compatibility between power sources and loads. For instance, in renewable energy systems, power converters are crucial for converting the variable output of solar panels or wind turbines into stable, usable electricity for homes and businesses. In consumer electronics, they adapt the mains electricity supply to the specific voltage and current needs of devices such as laptops, smartphones, and LED lights. All these applications of power converters need the design of an appropriate control law. Control of power converter is a challenging problem due to the presence of switching devices which are difficult to handle using traditional control approaches. The objective of this thesis is to investigate the use of data-driven techniques in the context of power converters feedback control. This study considers two examples of power converters: a boost converter with only one switch, and a power converter circuit provided by the OwnTech foundation, which consists of two parallel legs, each one has its own duty-cycle. The converters models are built and studied using a common technique called state space averaging, which allow us to mathematically describe the power converter circuit as a dynamical system in a state-space form suitable for the design of a feedback controller. The controller is then designed using Virtual Reference Feedback (VRFT) and Set-Membership (SM).