Nonlinear Adaptive Control: Regulation-Tracking-Oscillations

Fradkov, Alexander L.

doi:10.1016/s1474-6670(17)47680-4

Cited by 19 publications

(8 citation statements)

References 21 publications

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“…The approach to modification of the relative-degree and SPR properties is related to the 'parallel feedforward' as proposed in the context of adaptive control [35]. Another related idea is passification by means of shunting introduced by Fradkov [36]. All these approaches represent derivation of a loop-transfer function with SPR properties for a control object without SPR properties by means of dynamic extensions or observers.…”

Section: Discussionmentioning

confidence: 99%

Observer-based strictly positive real (SPR) variable structure output feedback control

Johansson

Robertsson

Shiriaev

2016

Journal of the Franklin Institute

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This paper considers switching output feedback control of linear systems and variablestructure systems. Theory for stability analysis and design for a class of observer-based feedback control systems is presented. A circle-criterion approach can be used to design an observer-based state feedback control which yields a closed-loop system with specified robustness characteristics. The approach is relevant for variable structure system design with preservation of stability when switching feedback control or sliding mode control is introduced in the feedback loop. It is shown that there exists a Lyapunov function valid over the total operating range and this Lyapunov function has also interpretation as a storage function of passivity-based control. The Lyapunov function can be found by solving a Lyapunov equation, which also generates variable structure switching surfaces. Important applications are to be found in variable structure systems with high robustness requirements.

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Section: Discussionmentioning

confidence: 99%

Observer-based strictly positive real (SPR) variable structure output feedback control

Johansson

Robertsson

Shiriaev

2016

Journal of the Franklin Institute

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Section: Discussionmentioning

confidence: 99%

Observer-based strictly positive real (SPR) variable structure output feedback control

Johansson

Robertsson

Shiriaev³

2012

2012 12th International Workshop on Variable Structure Systems

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“…For example, following a formulation that appeared in the Russian literature in the late 1960s (see, eg, the works of Yakubovich and Fradkov), we can state the model‐based nonlinear adaptive control problem as follows: consider a nonlinear plant modeled in the state space by the nonlinear differential equation, ie,

\begin{matrix} alignleft \\ align-1 \dot{x} & align-2 = F (t, x, u, p), \\ align-1 y & align-2 = G (t, x, u, p), \end{matrix}

where

t \in R^{+}, x \in R^{n}, y \in R^{m}, and u \in R^{n_{c}}

are the scalar time variable, the state vector, the output vector, and the control vector, respectively.

p \in \subset scriptP \in R^{p}

represents a vector of unknown parameters, element of an a priori known set

scriptP

, and F and G are two smooth functions.…”

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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Nonlinear Adaptive Control: Regulation-Tracking-Oscillations

Cited by 19 publications

References 21 publications

Observer-based strictly positive real (SPR) variable structure output feedback control

Observer-based strictly positive real (SPR) variable structure output feedback control

Observer-based strictly positive real (SPR) variable structure output feedback control

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

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