This paper focuses on the experimental identification of a proton exchange membrane water electrolyzer models. Two types of approaches are investigated: temporal and frequency domains identification techniques. The obtained models are validated and compared. A discussion is then provided to highlight the implications of using these identified models, particularly with respect to the choice of operating points, the impact of aging on model complexity, and their relevance for control and diagnostic purposes.

This paper deals with the simultaneous estimation of parameters and state variables for a class of algebro-differential linear parameter-varying (S-LPV) systems by employing a generalized dynamic adaptive observer. Sufficient conditions for the existence of the observer, ensuring stability with respect to linear matrix inequalities (LMIs) constraints through the utilization of Lyapunov stability theory, are provided. The main contribution of this paper is a methodology that generalizes proportional and proportional-integral adaptive observers. Furthermore, it enables a degree of robustness against uncertainties and modeling inaccuracies, while also enhancing steady-state accuracy. To demonstrate the observer performance, an academic example is included.

In this note, we design an invariant observer for a two step (acidogenesis-methanogenesis) mass balance non linear model, in order to estimate simultaneously all bacteria and substrate concentrations found in the anaerobic digestion process. The particularity of the designed observer is the use of only the methane flow rate which is cheap to measure and commonly measured online even at industrial scale.

In this paper, we propose to control the quantity and quality of the produced biogas from the anaerobic digestion of organic matter, digested in either a continuous stirred tank reactor or a fixed bed digester. This is motivated by the aim of providing the power grid with a stable amount of energy despite fluctuations in the treated waste concentration and composition. Therefore, we apply the linearizing control principe to a two step (acidogenesis-methanogenesis) mass balance non linear model, all with the introduction of two new control inputs reflecting the addition of stimulating substrates (acetate and alkalinity). To show the performance of the synthesized control laws we simulate the process under an organic shock load feeding.

In this paper an H∞decentralized observer-based control is proposed for large-scale uncertain nonlinear systems. These systems are coupled by N interconnected subsystems where the interconnections satisfy the quadratic constraints. The proposed control is based on a new form of dynamic observer (DO) which generalizes the existing results on the proportional observer (PO) and the proportional integral observer (PIO). The design approach is derived from the solution of matrix inequality and based on the algebraic constraints obtained from the analysis of the estimation error. A numerical example is provided to show the effectiveness of the proposed control.