Advanced predictive control techniques for electric drives applied to sustainable mobility and industry applications

Given the increasing global energy consumption and the critical role of electric motors in various sectors, improvements in reducing energy consumption are of paramount importance. The adoption of advanced control strategies opens new opportunities from a control perspective, allowing for high performance and energy efficiency. The proposed thesis explores predictive control techniques for electric drive applications. The research addresses two main challenges in electric motor drive systems: the development of a novel Model Predictive Control (MPC) solver and the design of innovative torque control strategies. The first part of the work focuses on designing an efficient MPC solver suitable for real-time applications in power electronics and electric motor drives. The proposed solver minimizes computational time, making it applicable to low-cost control platforms. This allows the adoption of MPC in a wider range of industrial applications where budget constraints are a factor. Two versions of the solver are presented: one for standard MPC formulation and another for a full-incremental approach aimed at offset-free reference tracking. The second part introduces novel paradigms for torque control in electric machines. Leveraging the flexibility of MPC, these strategies aim to maximize machine performances while maintaining working conditions at maximum efficiency. Experimental validation demonstrates the effectiveness of these techniques compared to traditional control solutions.