Preprint Article Version 1 This version is not peer-reviewed

A Self-Tuning NPID Control Method for FOPTD Processes

Version 1 : Received: 22 April 2020 / Approved: 23 April 2020 / Online: 23 April 2020 (10:24:14 CEST)

How to cite: Garbaabaa, H.A.; Geda, M.G.; Wase, M.G.; Ranganathan, S.; Jin, G.; Son, Y. A Self-Tuning NPID Control Method for FOPTD Processes. Preprints 2020, 2020040410 (doi: 10.20944/preprints202004.0410.v1). Garbaabaa, H.A.; Geda, M.G.; Wase, M.G.; Ranganathan, S.; Jin, G.; Son, Y. A Self-Tuning NPID Control Method for FOPTD Processes. Preprints 2020, 2020040410 (doi: 10.20944/preprints202004.0410.v1).

Abstract

Owing to the time-varying characteristics and nonlinearities of industrial processes, control has higher difficulties and results in challenges for advanced technology. In this paper, a self-tuning controller that includes a nonlinear proportional-integral-derivative (NPID) control function as well as a self-tuning function is proposed for first-order plus time delay (FOPTD) process control. The NPID control function is implemented using the nonlinear PID controller whose optimum parameters are adapted by a neural network (NN). The self-tuning function is able to identify the process dynamics using a short period of process behavior and tune NPID parameters based on the identified parameters. The advantage of the proposed method is validated with a set of simulation works on three processes and the comparison results are presented.

Subject Areas

self-tuning control; NPID controller; neural network; FOPTD process; genetic algorithm; parameter estimation

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