Abstract
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.
Highlights
Accurate dynamical models of complex systems are required for model-based controller design, analysis of the dynamic behavior of the system under study and improved system design
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
We present a parametric identification approach for a class of continuous-time single-input single-output (SISO) Lur’e-type systems that: (i) guarantees the identified model to be exponentially convergent and, (ii) uses numerically efficient tools to compute model responses and gradient information to be used for gradient-based optimization
Summary
Accurate dynamical models of complex systems are required for model-based controller design, analysis of the dynamic behavior of the system under study and improved system design. We present a parametric identification approach for a class of continuous-time single-input single-output (SISO) Lur’e-type systems that: (i) guarantees the identified model to be exponentially convergent and, (ii) uses numerically efficient tools to compute model responses and gradient information to be used for gradient-based optimization. This paper includes a statistical consistency analysis of the estimator, a detailed overview of methods to obtain an initial convergent model, a novel simulation case study and an experimental validation of the proposed identification approach.
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