Controlling the chaotic discrete-Hénon system using a feedforward neural network with an adaptive learning rate

Gokce, Kursad; Uyaroğlu, Yılmaz

doi:10.3906/elk-1109-58

Turkish Journal of Electrical Engineering and Computer Sciences

2013

DOI: 10.3906/elk-1109-58

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Controlling the chaotic discrete-Hénon system using a feedforward neural network with an adaptive learning rate

Kursad Gokce¹,

Yılmaz Uyaroğlu²

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Cited by 2 publications

References 16 publications

(12 reference statements)

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Controlling chaotic maps using next-generation reservoir computing

Kent,

Barbosa,

Gauthier

2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic Hénon map, including controlling the system between unstable fixed points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only ten data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error.

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Controlling chaotic maps using next-generation reservoir computing

Kent,

Barbosa,

Gauthier

2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Design of a new nonlinear model predictive fault tolerant control system using multi-sensor data fusion technique based on UKF algorithm

Ghesmat

Khalkhali

2015

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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Purpose – There are high expectations for reliability, safety and fault tolerance are high in chemical plants. Control systems are capable of potential faults in the plant processing systems. This paper proposes is a new Fault Tolerant Control (FTC) system to identify the probable fault occurrences in the plant. Design/methodology/approach – A Fault Diagnosis and Isolation (FDI) module has been devised based on the estimated state of system. An Unscented Kalman Filter (UKF) is the main innovation of the FDI module to identify the faults. A Multi-Sensor Data Fusion algorithm is utilized to integrate the UKF output data to enhance fault identification. The UKF employs an augmented state vector to estimate system states and faults simultaneously. A control mechanism is designed to compensate for the undesirable effects of the detected faults. Findings – The performance of the Nonlinear Model Predictive Controller (NMPC) without any fault compensation is compared with the proposed FTC scheme under different fault scenarios. Analysis of the simulation results indicates that the FDI method is able to identify the faults accurately. The proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated. Originality/value – A significant contribution of the paper is the design of an FTC system by using UKF to estimate faults and enhance the accuracy of data. This is done by applying a data fusion algorithm and controlling the system by the NMPC after eliminating the effects of faults.

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Controlling the chaotic discrete-Hénon system using a feedforward neural network with an adaptive learning rate

Cited by 2 publications

References 16 publications

Controlling chaotic maps using next-generation reservoir computing

Controlling chaotic maps using next-generation reservoir computing

Design of a new nonlinear model predictive fault tolerant control system using multi-sensor data fusion technique based on UKF algorithm

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