Robust Pole Assignment for Synthesizing Feedback Control Systems Using Recurrent Neural Networks

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2013.2275732 ◽

2014 ◽

Vol 25 (2) ◽

pp. 383-393 ◽

Author(s):

Xinyi Le ◽

Jun Wang

Keyword(s):

Neural Networks ◽

Feedback Control ◽

Control Systems ◽

Recurrent Neural Networks ◽

Pole Assignment ◽

Feedback Control Systems

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Global exponential stability of recurrent neural networks for synthesizing linear feedback control systems via pole assignment

IEEE Transactions on Neural Networks ◽

10.1109/tnn.2002.1000129 ◽

2002 ◽

Vol 13 (3) ◽

pp. 633-644 ◽

Author(s):

Yunong Zhang ◽

Jun Wang

Keyword(s):

Neural Networks ◽

Feedback Control ◽

Exponential Stability ◽

Control Systems ◽

Recurrent Neural Networks ◽

Global Exponential Stability ◽

Pole Assignment ◽

Linear Feedback ◽

Linear Feedback Control ◽

Feedback Control Systems

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Design of robust multivariable feedback control systems using pole assignment and positivity concepts

2001 European Control Conference (ECC) ◽

10.23919/ecc.2001.7076484 ◽

2001 ◽

Author(s):

G. Schmitt-Braess

Keyword(s):

Feedback Control ◽

Control Systems ◽

Pole Assignment ◽

Feedback Control Systems ◽

Multivariable Feedback

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Special issue on neural networks for feedback control systems

IEEE Transactions on Neural Networks ◽

10.1109/tnn.2005.861032 ◽

2005 ◽

Vol 16 (6) ◽

pp. 1717-1717

Keyword(s):

Neural Networks ◽

Feedback Control ◽

Control Systems ◽

Special Issue ◽

Feedback Control Systems

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Stability Analysis for State Feedback Control Systems Established as Neural Networks with Input Constraints

10.5220/0010548800002994 ◽

2021 ◽

Author(s):

Lukas Markolf ◽

Olaf Stursberg

Keyword(s):

Neural Networks ◽

Stability Analysis ◽

Feedback Control ◽

Control Systems ◽

State Feedback ◽

State Feedback Control ◽

Input Constraints ◽

Feedback Control Systems

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Special issue on neural networks for feedback control systems

IEEE Transactions on Neural Networks ◽

10.1109/tnn.2005.857295 ◽

2005 ◽

Vol 16 (5) ◽

pp. 1304-1304

Keyword(s):

Neural Networks ◽

Feedback Control ◽

Control Systems ◽

Special Issue ◽

Feedback Control Systems

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A MULTILAYER RECURRENT NEURAL NETWORK FOR REAL-TIME ROBUST POLE ASSIGNMENT IN SYNTHESIZING OUTPUT FEEDBACK CONTROL SYSTEMS

IFAC Proceedings Volumes ◽

10.3182/20020721-6-es-1901.00698 ◽

2002 ◽

Vol 35 (1) ◽

pp. 271-276

Author(s):

Sanqing Hu ◽

Jun Wang

Keyword(s):

Neural Network ◽

Feedback Control ◽

Control Systems ◽

Real Time ◽

Recurrent Neural Network ◽

Output Feedback ◽

Output Feedback Control ◽

Pole Assignment ◽

Feedback Control Systems

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Multilayer Recurrent Neural Networks for Synthesizing and Tuning Linear Control Systems via Pole Assignment

Control and Dynamic System - Neural Network Systems Techniques and Applications ◽

10.1016/s1874-5946(98)80082-5 ◽

1998 ◽

pp. 75-126 ◽

Author(s):

Jun Wang

Keyword(s):

Neural Networks ◽

Control Systems ◽

Recurrent Neural Networks ◽

Pole Assignment ◽

Linear Control ◽

Linear Control Systems

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A neurodynamic optimization approach to robust pole assignment for synthesizing linear state feedback control systems

52nd IEEE Conference on Decision and Control ◽

10.1109/cdc.2013.6760967 ◽

2013 ◽

Author(s):

Xinyi Le ◽

Jun Wang

Keyword(s):

Feedback Control ◽

Control Systems ◽

State Feedback ◽

Pole Assignment ◽

State Feedback Control ◽

Optimization Approach ◽

Feedback Control Systems ◽

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Design of Robust Multivariable Feedback Control Systems Using Pole Assignment and Positivity Concepts

European Journal of Control ◽

10.3166/ejc.9.384-391 ◽

2003 ◽

Vol 9 (4) ◽

pp. 384-391

Author(s):

G. Schmitt-Braess

Keyword(s):

Feedback Control ◽

Control Systems ◽

Pole Assignment ◽

Feedback Control Systems ◽

Multivariable Feedback

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Special issue on neural networks for feedback control systems

IEEE Transactions on Neural Networks ◽

10.1109/tnn.2005.853433 ◽

2005 ◽

Vol 16 (4) ◽

pp. 1009-1009

Keyword(s):

Neural Networks ◽

Feedback Control ◽

Control Systems ◽

Special Issue ◽

Feedback Control Systems

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