Control Systems and Identification

This cluster of papers covers advances in system identification techniques, including parameter estimation for nonlinear models, data-driven control, model-based control, feedback controllers, state estimation for multivariable systems, and the development of recursive algorithms. The papers also discuss model selection approaches and optimal experiment design for system identification.