Observer‐based adaptive control using multiple‐models switching and tuning

Giovanini, Leonardo; Sánchez, G.; Benosman, Mouhacine

doi:10.1049/iet-cta.2013.0242

Cited by 10 publications

(4 citation statements)

References 40 publications

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“…We can also cite here works that focus on designing model‐based adaptive controllers for linear systems under input constraints) or in infinite dimension . Furthermore, we could classify as direct linear adaptive control the concept of multiple‐model adaptive switching control, where a set of robust linear controllers is designed to deal with a range of plant uncertainties, and a supervisor is used to switch online between these controllers, while maintaining performance and stability …”

Section: Model‐based Adaptive Controlmentioning

confidence: 99%

Model‐based vs data‐driven adaptive control: An overview

Benosman

2018

Adaptive Control & Signal

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In this paper, we present an overview of adaptive control by contrasting model-based approaches with data-driven approaches. Indeed, we propose to classify adaptive controllers into two main subfields, namely, model-based adaptive control and data-driven adaptive control. In each subfield, we cite monographs, survey papers, and recent research papers published in the last few years. We also include a few simple examples to illustrate some general concepts in each subfield.

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Section: Model‐based Adaptive Controlmentioning

confidence: 99%

Model‐based vs data‐driven adaptive control: An overview

Benosman

2018

Adaptive Control & Signal

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“…Thus, in order to find the optimal task values of the regulators in the regulation circuits, it is required to know the functional dependences between the above-mentioned parameters depending on the available stimulating influences [10]. This, in turn, requires operative measurement of the factual values of all parameters characterizing the current technological progress of the process [11]. In addition, taking into account the fact that technological facilities operate under the influence of various stimulating factors, correction and adaptation of the parameters of previously obtained initial functional dependencies is required over time, which leads to additional difficulties [12].…”

Section: Introductionmentioning

confidence: 99%

Development of adaptive control system for technological facility of primary oil refining

Safarova,

Damirova,

Huseynov

2023

Eureka: PE

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As it is known, primary oil processing facilities are designed for the production of light-colored oil products and various oil fractions that meet modern requirements with their quality. The main equipment of these technological facilities are rectification columns operating under atmospheric and vacuum conditions, which perform the decomposition of crude oil and fuel oil into various product fractions. Generally, the quality indicators of product fractions produced in these columns are provided by stabilizing the temperature modes at certain points (plates) of these or rectification columns by regulating the irrigation consumption supplied to those plates. It should be noted that currently, in accordance with the requirements of the time, the design and development of new control systems that can adequately respond to more dynamic and variable external and internal stimulating influences for complex oil refining technological facilities is one of the main problems facing the automatic control theory. The paper is dedicated to the issue of developing a new automated control system that can provide the required accuracy in terms of quality management. As an adaptive control system, the purpose of such systems is to compensate in time for disturbance influences in the technological devices and to ensure the production of oil products with stable quality indicators, regardless of these influences. The purpose of study is to develop a control system that can meet the given requirements and ensure the produced products with relatively stable quality indicators, regardless of the controlled and uncontrolled exciting effects affecting the oil refinery technological complex. The principle of operation of this system is based not only on the compensation of disturbance influences but also on the calculation of new operation strategies depending on these effects. The development of new automated control system that is able to provide the necessary accuracy of quality control in the lack of information is relevant

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“…For this reason, many control structures such as model reference adaptive control (MRAC), self-tuning control (STC) and gain scheduling control (GSC) are proposed [15]- [17]. The theory of using multiple models is introduced and employed using multiple Kalman filters, switching and switching and tuning strategies [12], [18]- [24]. In all these methods, a large number of models are generated in the bank, but at every time the control input is completely based on the closest model to the plant.…”

Section: Introductionmentioning

confidence: 99%

Multiple model adaptive control using second level adaptation for a delta-wing aircraft

Ahmadian

Khosravi

Sarhadi

2015

International Journal of Intelligent Unmanned Systems

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Purpose – The purpose of this paper is to design a stable controller such that the control input is applied to the delta-wing aircraft in order to adjust the roll dynamics. The controller must provide a desired tracking performance with minimum tracking error. Design/methodology/approach – In this paper, the second level adaptation (SLA) strategy is applied to control a delta-wing aircraft using multiple models. The implemented control structure is compared with the first level adaptation (FLA) and model reference adaptive control (MRAC) techniques. Findings – SLA architecture not only copes with a wide uncertainty domain caused by aerodynamic effects, but also its rapid and accurate convergence is one of its most important features. Furthermore, this strategy makes a smoother control signal with respect to FLA and MRAC even at the same initial times. It should be also noted that SLA using three models, copes with uncertainty that may occur to the aircraft at high Angle Of Attacks (AOAs) at the entire flight envelope. Originality/value – In this paper for the first time the application of this strategy is used to identify and control a delta-wing aircraft. Furthermore a systematic block diagram approach is proposed for the design.

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Observer‐based adaptive control using multiple‐models switching and tuning

Cited by 10 publications

References 40 publications

Model‐based vs data‐driven adaptive control: An overview

Model‐based vs data‐driven adaptive control: An overview

Development of adaptive control system for technological facility of primary oil refining

Multiple model adaptive control using second level adaptation for a delta-wing aircraft

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