Intrinsic Plasticity-Based Neuroadptive Control with Both Weights and Excitability Tuning

Qing CHEN; Anguo ZHANG; Yongduan SONG

doi:10.1109/TNNLS.2020.3011044

Intrinsic Plasticity-Based Neuroadptive Control with Both Weights and Excitability Tuning

Qing CHEN
, Anguo ZHANG
, Yongduan SONG^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

10 Citations (Scopus)

Abstract

This brief presents an intrinsic plasticity (IP)-driven neural-network-based tracking control approach for a class of nonlinear uncertain systems. Inspired by the neural plasticity mechanism of individual neuron in nervous systems, a learning rule referred to as IP is employed for adjusting the radial basis functions (RBFs), resulting in a neural network (NN) with both weights and excitability tuning, based on which neuroadaptive tracking control algorithms for multiple-input-multiple-output (MIMO) uncertain systems are derived. Both theoretical analysis and numerical simulation confirm the effectiveness of the proposed method.

Original language	English
Article number	9159903
Pages (from-to)	3282-3286
Number of pages	5
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	32
Issue number	7
DOIs	https://doi.org/10.1109/TNNLS.2020.3011044
Publication status	Published - Jul 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61803053, Grant 61833013, Grant 61860206008, and Grant 61773081; and in part by the Graduate Scientific Research and Innovation Foundation of Chongqing under Grant CYB19056 and Grant CYB19057.

Keywords

Intrinsic plasticity (IP)
neuroadaptive control
nonlinear systems
radial basis function neural network (RBFNN)

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10.1109/TNNLS.2020.3011044

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title = "Intrinsic Plasticity-Based Neuroadptive Control with Both Weights and Excitability Tuning",

abstract = "This brief presents an intrinsic plasticity (IP)-driven neural-network-based tracking control approach for a class of nonlinear uncertain systems. Inspired by the neural plasticity mechanism of individual neuron in nervous systems, a learning rule referred to as IP is employed for adjusting the radial basis functions (RBFs), resulting in a neural network (NN) with both weights and excitability tuning, based on which neuroadaptive tracking control algorithms for multiple-input-multiple-output (MIMO) uncertain systems are derived. Both theoretical analysis and numerical simulation confirm the effectiveness of the proposed method.",

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author = "Qing CHEN and Anguo ZHANG and Yongduan SONG",

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AB - This brief presents an intrinsic plasticity (IP)-driven neural-network-based tracking control approach for a class of nonlinear uncertain systems. Inspired by the neural plasticity mechanism of individual neuron in nervous systems, a learning rule referred to as IP is employed for adjusting the radial basis functions (RBFs), resulting in a neural network (NN) with both weights and excitability tuning, based on which neuroadaptive tracking control algorithms for multiple-input-multiple-output (MIMO) uncertain systems are derived. Both theoretical analysis and numerical simulation confirm the effectiveness of the proposed method.

KW - Intrinsic plasticity (IP)

KW - neuroadaptive control

KW - nonlinear systems

KW - radial basis function neural network (RBFNN)

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DO - 10.1109/TNNLS.2020.3011044

M3 - Journal Article (refereed)

AN - SCOPUS:85111951912

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EP - 3286

JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

IS - 7

M1 - 9159903

ER -

Intrinsic Plasticity-Based Neuroadptive Control with Both Weights and Excitability Tuning

Abstract

Bibliographical note

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Keywords

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