A filtered high gain observer for a class of non uniformly observable systems – Application to a phytoplanktonic growth model

Robles-Magdaleno, J.L.; Rodríguez-Mata, Abraham Efraim; Farza, M.; M’Saad, M.

doi:10.1016/j.jprocont.2020.01.007

Cited by 14 publications

(17 citation statements)

References 22 publications

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“…Mandenius and Gustavsson (11) reviewed several software sensor solutions that are used to derive new information from true sensors by combining them with software models. These models range from high mathematical signal processing complexity (12)(13)(14) to those with emphasis on biological system modeling that describe specific biological systems (15,16). A general downside of these models is that either their implementation is of such complexity that they are difficult to realize and verify or that they are not suited for a general solution.…”

Section: Introductionmentioning

confidence: 99%

System characterization of dynamic biological cultivations through improved data analysis

Stouten

Douwenga²,

Hogendoorn

et al. 2021

Preprint

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Determining the functional development and dominant competitive strategy in microbial community enrichments is complicated by the extensive measurement campaigns required for off-line system analysis. This study demonstrates that detailed system characterization of aerobic pulse fed enrichments can be established using on-line measurements combined with automated data analysis. By incorporating the physicochemical processes in on-line data processing with a Particle Filter and kinetic process model, an accurate reconstruction of the dominant biological rates can be made. We hereby can differentiate between storage compound production and biomass growth in sequencing batch bioreactors. The method proposed allows for close monitoring of changes in functional behavior of long-running enrichment cultures, without the need for off-line samples, therewith enabling the identification of new insights in process dynamics with a minimal experimental effort. Even though a specific example application of the method proposed is described here, the approach can readily be extended to a wide range of dynamic experimental systems that can be characterized based on on-line measurements.

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Section: Introductionmentioning

confidence: 99%

System characterization of dynamic biological cultivations through improved data analysis

Stouten

Douwenga²,

Hogendoorn

et al. 2021

Preprint

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“…Additionally, in order to provide a filtered observer, the following classical assumptions are considered (see [13])…”

Section: Design Of Filtered Observermentioning

confidence: 99%

“…Given the complexity and its enormous applications, this research is still open [9]- [12]. To the best of our knowledge, there are few works oriented to the use of the novel filtered observers to applications in drones or quadrotors as [13].…”

Section: Introductionmentioning

confidence: 99%

“…It is noteworthy that several relatively recent works have been focused on the improvement of the high-gain observer performances, especially in the presence of measurement noises, by proposing versions with a time-varying design parameter or by slightly changing the structure of the gain in the observer [13], [28], [29]. This new work falls within the framework of this last approach.…”

Section: Introductionmentioning

confidence: 99%

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Filtered Observer-Based IDA-PBC Control for Trajectory Tracking of a Quadrotor

et al. 2021

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In this paper, a new filtered observer-based IDA-PBC (Interconnection and Damping Assignment-Passivity Based Control) strategy is developed for trajectory tracking of a quadrotor in the presence of disturbances and model uncertainties. The proposed algorithm allows the control of the quadrotor in all its states. It can deal with the noisy output measurements and uncertainties in the translational and rotational dynamics, as unmodeled dynamics inherent to real systems or unknown external signals (exogenous signals). The designed filtered observer estimates the state from noisy output measurements, and it depends only on two design parameters. Numerical simulation tests are carried out to highlight the overall controller approach in a realistic scenario.

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“…Observers have received several versions and improvements in their performance, over time, in the difficult task of state estimation and parameters in complex systems, but these are still being improved in all their different applications. For example, some outstanding works of each technique, from the first generations linear [21], non-linear [22], adaptive [23], sliding mode [24], non-uniformly observable [25], and discreet observers who can provide good results [26], based on the frequency [27] and high-gain systems [28], have been proposed. There are also applications of its functions and fuzzy techniques for diagnosing sensor faults and multiple applications [29,30].…”

Section: Introductionmentioning

confidence: 99%

A Fractional High-Gain Nonlinear Observer Design—Application for Rivers Environmental Monitoring Model

Rodríguez-Mata

Bustos-Terrones

Gonzalez-Huitron

et al. 2020

MCA

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The deterioration of current environmental water sources has led to the need to find ways to monitor water quality conditions. In this paper, we propose the use of Streeter–Phelps contaminant distribution models and state estimation techniques (observer) to be able to estimate variables that are very difficult to measure in rivers with online sensors, such as Biochemical Oxygen Demand (BOD). We propose the design of a novel Fractional Order High Gain Observer (FOHO) and consider the use of Lyapunov convergence functions to demonstrate stability, as it is compared to classical extended Luenberger Observer published in the literature, to study the convergence in BOD estimation in rivers. The proposed methodology was used to estimated Dissolved oxygen (DO) and BOD monitoring of River Culiacan, Sinaloa, Mexico. The use of fractional order in high-gain observers has a very effective effect on BOD estimation performance, as shown by our numerical studies. The theoretical results have shown that robust observer design can help solve problems in estimating complex variables.

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A filtered high gain observer for a class of non uniformly observable systems – Application to a phytoplanktonic growth model

Cited by 14 publications

References 22 publications

System characterization of dynamic biological cultivations through improved data analysis

System characterization of dynamic biological cultivations through improved data analysis

Filtered Observer-Based IDA-PBC Control for Trajectory Tracking of a Quadrotor

A Fractional High-Gain Nonlinear Observer Design—Application for Rivers Environmental Monitoring Model

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