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Article | Green Intelligent Systems and Applications | Volume 2 - Issue 2 - 2022 | 84-95 | https://doi.org/10.53623/gisa.v2i2.106
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Study on Setpoint Tracking Performance of the PID SISO and MIMO Under Noise and Disturbance for Nonlinear Time-Delay Dynamic Systems

Author(s): Ali Rospawan , Yukai Yang , Po-Hsu Chen , Ching-Chih Tsai
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Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan

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Green Intelligent Systems and Applications

Volume 2 - Issue 2 - 2022

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This paper presents a case study of the setpoint tracking performance of the proportional integral derivative (PID) controller on the Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) nonlinear digital plants under Gaussian white noise and constant load disturbance for the nonlinear time-delay dynamic system. With the objective of getting a better understanding of the nonlinear discrete-time PID controller, we proposed a case study using two SISO and two MIMO digital plants, and then do the numerical simulations along with the addition of Gaussian white noise and load disturbance to simulate the real environment. In this paper, we compare the results of the system working with and without noise and load disturbance. The study result of this paper shows that on the discrete-time digital nonlinear plant, the PID controller is working well to follow the nonlinear setpoint even under heavy noise and load disturbance. The study compared the performance indexes of the controllers in terms of the maximum error, the Root mean square error (RMSE), the Integral square error (ISE), the Integral absolute error (IAE), and the Integral of time-weighted absolute error (ITAE).
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SUBMITTED: 16 July 2022
ACCEPTED: 02 October 2022
PUBLISHED: 9 October 2022
SUBMITTED to ACCEPTED: 78 days
DOI: https://doi.org/10.53623/gisa.v2i2.106

Cite this article
Rospawan, A., Yang, Y., Chen, P.-H., & Tsai, C.-C. (2022). Study on Setpoint Tracking Performance of the PID SISO and MIMO Under Noise and Disturbance for Nonlinear Time-Delay Dynamic Systems. Green Intelligent Systems and Applications, 2(2), 84–95. https://doi.org/10.53623/gisa.v2i2.106
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