Dr. Hollweg’s main research areas include Adaptive Control Theory, Nonlinear Control, Optimization Algorithms, and Control Applications in Power Electronics and Motor Drives. In the context of control design and optimization, AI techniques are frequently explored—not only for data-driven analysis but also for controller design itself.
Methods such as gradient-based and recursive least squares (RLS)-based adaptive control, as well as metaheuristic algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Bayesian Optimization, are commonly employed. Additionally, Neural Networks and Reinforcement Learning have proven to be powerful tools for developing more robust and optimal control policies aiming to bridge the gap between Lyapunov-based control theory and data-driven techniques.
