Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm / (Record no. 96187)

MARC details
000 -LEADER
fixed length control field 04914nam a2200349 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field MY-KuUP
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251125105937.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field a||||fr|||| 001 0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field ta
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220216t20202020my a|||frma|| 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number THE0008588(Local)
040 ## - CATALOGING SOURCE
Original cataloging agency UMP
Language of cataloging eng
Transcribing agency UMP
Description conventions rda
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) FTKEE .R53 2020 r Thesis
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Mohd Riduwan Ghazali,
Relator term author.
245 10 - TITLE STATEMENT
Title Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm /
Statement of responsibility, etc. Mohd Riduwan Ghazali
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Kuantan, Pahang :
Name of producer, publisher, distributor, manufacturer UMP,
Date of production, publication, distribution, manufacture, or copyright notice 2020
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture © 2020
300 ## - PHYSICAL DESCRIPTION
Extent xvi, 116 pages :
Other physical details illustrations some color ;
Dimensions 30 cm. +
Accompanying material 1 CD ROM
336 ## - CONTENT TYPE
Content type term text
Source rdacontent
337 ## - MEDIA TYPE
Media type term unmediated
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term volume
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
500 ## - GENERAL NOTE
General note Faculty of Electrical & Electronics Engineering
502 ## - DISSERTATION NOTE
Dissertation note Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2021
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references
520 3# - SUMMARY, ETC.
Summary, etc. This study focused on data-driven tools and controller structure in the data-driven control scheme. Data-driven tools are an optimization method to find the optimal controller parameters using the system’s input and output data. Meanwhile, the controller structure refers to the controller design that is highly dependent on the input and output system. The existing data-driven neuroendocrine-PID (NEPID) utilizes the simultaneous perturbation stochastic approximation (SPSA) algorithm as the data-driven tool. However, this SPSA-based method is unable to find the optimal value of the design parameter due to unstable convergence obtained that degrades the controller performance in MIMO systems. Thus, a safe experimentation dynamics (SED) algorithm is selected to solve this unstable convergence but still not enough to achieve high accuracy because the update designed parameter only depends on the fixed step size gain. For the controller structure, the hormone secretion rate parameter of the existing NEPID is constant during the experimental time. However, control accuracy is insufficient because the secretion rate and control variable error are not able to interact directly and limits the controller capability. Besides, in the existing NEPID controller structure of the SISO system, only a single node of hormone regulation is used due to a single control variable. Meanwhile, in the MIMO systems, many control variables available that interact with each other, and the single node hormone regulation of NEPID is still inadequate in producing better control accuracy of nonlinear MIMO systems. Therefore, this study proposed the adaptive safe experimentation dynamics (ASED) algorithm to improve the SED algorithm performance accuracy by minimizing its objective function in terms of mean, best, worst, and standard deviation analysis. In order to increase the control accuracy of the existing NEPID controller, this study also established the sigmoid-based secretion rate neuroendocrine- PID (SbSR-NEPID) controller structure by varying the hormone secretion rate according to the change of error. Finally, this study also focused on developing a multiple node hormone regulation neuroendocrine-PID (MnHR–NEPID) controller structure to improve the control accuracy of existing NEPID by prioritizing the control regulation of each node from their level of error. The performance of PID and NEPID controllers was compared with those of SbSR-NEPID and MnHR-NEPID performances based on error and input tracking. The results show that the ASED- and SED-based methods produced stable convergence. The ASED-based method provided better tracking performance than the SED method by obtaining the objective function’s lower values. Besides, from the simulation work, the SbSR-NEPID and MnHR-NEPID designs provided better control accuracy in terms of lower objective function, total norm of error, and total norm of input compared to those of the PID and NEPID controllers. Moreover, the SbSR-NEPID controller achieved control accuracy improvement of 4.95% and 5.89% for the container gantry crane and TRMS systems, respectively. Besides, the MnHR-NEPID controller achieved control accuracy improvement of 5.7% and 5.1% for the container gantry crane and TRMS systems, respectively. The ASED-based method significantly improved the SED method’s accuracy by using adaptive terms based on changing the objective function in the updated procedure. Besides, the SbSR-NEPID was effective in reducing the error in a transient state, and MnHR-NEPID provided effective interaction between nodes available in MIMO systems which contributed to accuracy improvement.
610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Faculty of Electrical & Electronics Engineering
General subdivision Dissertations
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Universities and colleges
General subdivision Dissertations
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Theses
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Thesis
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
  Not lost Library of Congress Classification     Reference UMPLIB PEKAN UMPLIB PEKAN Reference 05/01/2022   FTKEE .R53 2020 r Thesis T000001495 31/03/2022 1 05/01/2022 Thesis
  Not lost Library of Congress Classification     Reference UMPLIB PEKAN UMPLIB PEKAN Reference 17/02/2022   FTKEE .R53 2020 r Thesis T000001443 31/03/2022 2 17/02/2022 Thesis
  Not lost Library of Congress Classification     Reference UMPLIB PEKAN UMPLIB PEKAN   17/02/2022   CD 12888 T000001444 17/02/2022 1 17/02/2022 Thesis

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