Computationally efficient identification of continuous-time Lur'e-type systems with stability guarantees

In this paper, we propose a parametric system identification approach for a class of continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we show that the steady-state model response and the gradient of the model response with respect to its parameters can be computed in a numerically fast and efficient way, allowing efficient use of global and local optimization methods to solve the identification problem. Furthermore, by enforcing the identified model to be inside the set of convergent models, we certify a stability property of the identified model, which allows for reliable generalized usage of the model also for other excitation signals than those used to identify the model. The effectiveness and benefits of the proposed approach are demonstrated in a simulation case study. Furthermore, we have experimentally shown that the proposed approach provides fast identification of both medical equipment and patient parameters in mechanical ventilation and, thereby, enables improved patient treatment