A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network
This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has h...
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| Format: | Electronic Book Chapter |
| Language: | English |
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2011
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| Online Access: | https://www.intechopen.com/chapters/15263 |
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| _version_ | 1839561757771694080 |
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| author | Anh, Ho Pham Huy Nam, Nguyen Thanh |
| author_facet | Anh, Ho Pham Huy Nam, Nguyen Thanh |
| author_sort | Anh, Ho Pham Huy |
| collection | InTech Open eBooks |
| description | This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization and flexibility in online-updating. The proposed adaptive algorithm focuses on fast and efficiently optimizing Gain Scheduling and PID weighting parameters of Neural MLPNN model used in DNN-PID controller. This approach is employed to implement the DNN-PID controller with a view of controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time through Real-Time Windows Target run in MATLAB SIMULINK® environment. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID controller. These results can be applied to control other highly nonlinear SISO and MIMO systems. Keywords: highly nonlinear PAM manipulator, proposed online tuning Gain Scheduling Dynamic Nonlinear PID controller (DNN-PID), real-time joint angle position control, fast online tuning back propagation (BP) algorithm, pneumatic artificial muscle (PAM) actuator. |
| doi_str_mv | 10.5772/15964 |
| first_indexed | 2025-08-04T21:26:41Z |
| format | Electronic Book Chapter |
| fullrecord | <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><identifier>InTech-15263</identifier><datestamp>2011-04-19</datestamp>
<dc:title>A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network</dc:title>
<dc:creator>Ho Pham Huy Anh</dc:creator>
<dc:creator>Nguyen Thanh Nam</dc:creator>
<dc:subject>Physical Sciences, Engineering and Technology</dc:subject>
<dc:description>This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization and flexibility in online-updating. The proposed adaptive algorithm focuses on fast and efficiently optimizing Gain Scheduling and PID weighting parameters of Neural MLPNN model used in DNN-PID controller. This approach is employed to implement the DNN-PID controller with a view of controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time through Real-Time Windows Target run in MATLAB SIMULINK® environment. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID controller. These results can be applied to control other highly nonlinear SISO and MIMO systems. Keywords: highly nonlinear PAM manipulator, proposed online tuning Gain Scheduling Dynamic Nonlinear PID controller (DNN-PID), real-time joint angle position control, fast online tuning back propagation (BP) algorithm, pneumatic artificial muscle (PAM) actuator.</dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2011-04-19</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/15263</dc:identifier>
<dc:identifier>doi:10.5772/15964</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-953-307-166-4</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by-nc-sa/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/125 ; PID Control, Implementation and Tuning</dc:source>
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| id | InTech-15263 |
| institution | Matheson Library |
| isbn | 978-953-307-166-4 |
| language | English |
| last_indexed | 2025-08-04T21:26:41Z |
| publishDate | 2011 |
| publisher | IntechOpen |
| record_format | intech |
| spelling | InTech-152632011-04-19 A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network Ho Pham Huy Anh Nguyen Thanh Nam Physical Sciences, Engineering and Technology This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization and flexibility in online-updating. The proposed adaptive algorithm focuses on fast and efficiently optimizing Gain Scheduling and PID weighting parameters of Neural MLPNN model used in DNN-PID controller. This approach is employed to implement the DNN-PID controller with a view of controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time through Real-Time Windows Target run in MATLAB SIMULINK® environment. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID controller. These results can be applied to control other highly nonlinear SISO and MIMO systems. Keywords: highly nonlinear PAM manipulator, proposed online tuning Gain Scheduling Dynamic Nonlinear PID controller (DNN-PID), real-time joint angle position control, fast online tuning back propagation (BP) algorithm, pneumatic artificial muscle (PAM) actuator. IntechOpen 2011-04-19 Chapter, Part Of Book https://www.intechopen.com/chapters/15263 doi:10.5772/15964 en ISBN:978-953-307-166-4 https://creativecommons.org/licenses/by-nc-sa/3.0/ https://www.intechopen.com/books/125 ; PID Control, Implementation and Tuning |
| spellingShingle | Physical Sciences, Engineering and Technology Anh, Ho Pham Huy Nam, Nguyen Thanh A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network |
| title | A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network |
| title_full | A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network |
| title_fullStr | A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network |
| title_full_unstemmed | A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network |
| title_short | A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network |
| title_sort | new approach of the online tuning gain scheduling nonlinear pid controller using neural network |
| topic | Physical Sciences, Engineering and Technology |
| url | https://www.intechopen.com/chapters/15263 |
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