Neuro-Fuzzy Modified Smith predictor for IPDT and FOPDT Processes Control

Chen, Hao; Zouaoui, Zoubir; Chen, Zheng

doi:10.3182/20130904-3-fr-2041.00093

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In the industrial field, Smith prediction compensation is mainly used to solve the pure lag of the system [10]. Guangda Chen [11] proposed a Smith predictor combined with Linear Active Disturbance Rejection Control (LADRC) and analyzed the stability of the Smith + LADRC time-delay control system from a theoretical point of view.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Liquid Fertilizer pH Regulation Controller Based on BP-PID-Smith Predictive Compensation Algorithm

Meng

Zhang

et al. 2022

Applied Sciences

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The pH value of liquid fertilizer is a key factor affecting crop growth, so it is necessary to regulate its pH value. However, the pH regulation system has the characteristics of nonlinearity and time lag, which makes it difficult for the conventional controller to achieve accurate pH control. By analyzing the regulation process, this paper designs a BP-PID-Smith prediction compensator, which compensates for the error between the actual model and the theoretical model and improves the control accuracy. The pH regulation system with STM32F103ZET6 as the control core was also developed, and the performance tests were carried out under different flow rates to compare with the regulation system of PID-Smith and Smith algorithms. The experimental results showed that the maximum overshoot of the BP-PID-Smith prediction compensator was 0.27% on average, and the average adjustment time for pH value reduction from 7.5 to 6.8 was 71.39 s, which had good practicality and robustness to meet the actual control demand.

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Section: Introductionmentioning

confidence: 99%

Design and Application of Liquid Fertilizer pH Regulation Controller Based on BP-PID-Smith Predictive Compensation Algorithm

Meng

Zhang

et al. 2022

Applied Sciences

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“…Apart from these studies, a gain-scheduled controller based on fuzzy techniques is presented by Özbek and Eker (2016b). Yet another work investigates various intelligent-based SP applications such as neuro-fuzzy Smith and so forth (Chen et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of various modified Smith predictor-based fractional order control design strategies in control of a thermal process with time delay

Özbek

�°

Eker

2019

IJES

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In this study, a number of modified Smith predictor (SP)-based fractional order control strategies are investigated experimentally on a thermal process. Controller design methods and tuning strategies are elaborated step by step. Further, control strategies are discussed in relation to design specifications, control cost, implementation issues, and operating modes encountered in practice. Performance comparison is presented via several illustrations and numerical measures. Particular attention is paid to the tracking precision, quality of the control signal, robustness against disturbances and energy efficiency. Complementary comments are addressed based on merits and demerits of the each control technique.

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Smith Predictor-based Controllers for Temperature Process with Time Delay

Iampita

Hisham

2019

2019 IEEE Student Conference on Research and Development (SCOReD)

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Neuro-Fuzzy Modified Smith predictor for IPDT and FOPDT Processes Control

Cited by 5 publications

References 16 publications

Design and Application of Liquid Fertilizer pH Regulation Controller Based on BP-PID-Smith Predictive Compensation Algorithm

Design and Application of Liquid Fertilizer pH Regulation Controller Based on BP-PID-Smith Predictive Compensation Algorithm

Experimental evaluation of various modified Smith predictor-based fractional order control design strategies in control of a thermal process with time delay

Smith Predictor-based Controllers for Temperature Process with Time Delay

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