Synthesis of absolutely stabilising controllers

Malik, Waqar; Kang, Sincheon; Darbha, Swaroop; Bhattacharyya, S.P.

doi:10.1049/iet-cta:20070201

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…In this example, we take the nodal dynamics as the model of a flexible link robotic arm, whose graphical illustration is shown in [8,28] . Each controlled robotic arm is a Lur'e subsystem in the form of (6) with matrices [28] A It is verified that each reduced-order Lur'e subsystem is finite-gain L 2 stable, and the a priori bound on the approximation of subsystems is computed as y i (t) −ˆ y i (t) 2 ≤ 0 . 3684 • u i (t) 2 .…”

Section: Examplementioning

confidence: 99%

Model reduction of synchronized homogeneous Lur’e networks with incrementally sector-bounded nonlinearities

Cheng

Scherpen

Zhang

2019

European Journal of Control

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Section: Examplementioning

confidence: 99%

Model reduction of synchronized homogeneous Lur’e networks with incrementally sector-bounded nonlinearities

Cheng

Scherpen

Zhang

2019

European Journal of Control

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Robust and nonlinear control literature survey (No. 10)

2009

Intl J Robust & Nonlinear

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In order to keep the readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of robust and nonlinear control six times a year. This list is comp iled from a wide range of journals. Please note that even though the list has been broadly categorized, these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Leonardo Giovanini at lgiovanini@fich.unl.edu.ar.

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ANN based adaptive controller tuned by RTRL algorithm for non-linear systems

Thampatty

Nandakumar

Cheriyan

2009

2009 2nd International Workshop on Nonlinear Dynamics and Synchronization

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The paper presents artificial neural network (ANN) based adaptive controller for nonlinear systems. A state feedback adaptive control algorithm using fully connected recurrent neural network is employed. The desired trajectory for the on-line training of the neural network is obtained from a reference model. The synaptic weights adaptation of the network is based on real time recurrent learning algorithm (RTRL). Since the synaptic weights are adjusted in real time, this novel method of controller design has potential applications in non-linear systems. Simulation results of the controller applied to a simple non-linear dynamic system demonstrate the effectiveness of the controller.Index Terms-Artificial neural network (ANN), Non-linear control system, Real time recurrent learning algorithm (RTRL).

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Synthesis of absolutely stabilising controllers

Cited by 3 publications

References 9 publications

Model reduction of synchronized homogeneous Lur’e networks with incrementally sector-bounded nonlinearities

Model reduction of synchronized homogeneous Lur’e networks with incrementally sector-bounded nonlinearities

Robust and nonlinear control literature survey (No. 10)

ANN based adaptive controller tuned by RTRL algorithm for non-linear systems

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