A predictive control based on decentralized fuzzy inference for a pH neutralization process

Wu, Hui; Yan, Fei; Wang, Guangjun; Lv, Cai

doi:10.1016/j.jprocont.2021.12.001

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…Let a series of estimates of the PDD be obtained using Algorithm (27) at moment k, establishing the auto-regressive equation of PPD:…”

Section: Model-free Adaptive Predictive Controlmentioning

confidence: 99%

“…Although the above control methods have been successfully applied to the actual system, they all rely on the accurate model of pH neutralization process, as we all know, the control object is becoming more and more complex, it is not easy to establish an accurate mathematical model of the controlled system, and the actual production process requires rich experience to debug the model, and the theoretical analysis is also difficult, making the establishment of the model sometimes even more difficult than the control itself. Adaptive control and predictive control are control theories and methods based on the accurate mathematical model of the controlled system, which have a strong dependence on the model quality of the control system, and can achieve good control under a single working condition or little change in the model [26,27]. pH neutralization process is highly nonlinear, and when the target parameters change over a large range, it is difficult for a fixed-modelbased controller to obtain good control results, so it has limitations in practical applications.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Method of Model-Free Adaptive Predictive Control: A Case of pH Neutralization in WWTP

Li¹,

Tang²,

Luan³

et al. 2023

Processes

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pH neutralization reaction process plays a crucial role in Waste Water Treatment Process (WWTP). Traditional PID Proportion Integral Differential, (or even advanced PID control) algorithms have poor performance on WWTP due to the strong non-linearity, large time lag, and large inertia characteristics of pH neutralization. Therefore, finding a superior control method to maintain the pH value of wastewater within the normal range will greatly help to improve the efficiency and effectiveness of wastewater treatment. The chemical reaction mechanism of pH neutralization reaction process is first analyzed, and a mechanistic model of pH neutralization reaction process is developed based on the reaction of ions during acid-alkali neutralization and the electric balance equation. Then, combining the characteristics of generalized predictive control and Model-Free Adaptive Control (MFAC), a Model-Free Adaptive Predictive Control (MFAPC) method based on compact format dynamic linearization is introduced. An Improved Model Free Adaptive PI Predictive Control algorithm (IMFAPC) with proportional (P) and integral (I) algorithms is proposed to further improve the control performance. IMFAPC is proposed on the basis of MFAPC, combining the advantages of generalized predictive control, introducing a PI module consisting of error and error sum, and predicting the PI module, making it possible to produce more accurate constraints on the control inputs, avoiding increasing errors, and improving the control effect of delayed systems at the same time. pH neutralization process simulation experimental results show that compared with the ordinary Model-Free Adaptive Control (MFAC) and MFAPC, the IMFAPC control algorithms has the best performance in terms of accuracy, overshoot, and the robustness.

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“…Let a series of estimates of the PDD be obtained using Algorithm (27) at moment k, establishing the auto-regressive equation of PPD:…”

Section: Model-free Adaptive Predictive Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Method of Model-Free Adaptive Predictive Control: A Case of pH Neutralization in WWTP

Li¹,

Tang²,

Luan³

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…Based on the traditional predictive control, a novel predictive control method based on decentralized fuzzy inference is proposed to deal with the strong nonlinearity, large hysteresis, and uncertainty problems in the pH neutralization process [12]. A neural PID control method was developed, and it performs well in the presence of obvious disturbances control performance [13].…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for Tricalcium Neutralization Process with Initial Disturbance

et al. 2023

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The tricalcium neutralization process (TNP) is a key recovery process in the citric acid production process. The amount of calcium carbonate added needs to be controlled to ensure the pH of the reactants within the target range at the terminal of every batch. But the random initial pH has a great influence on the stability of the terminal pH. In this work, an iterative learning control (ILC)‐based control strategy is proposed to optimize the addition of calcium carbonate. First, the terminal iterative learning control (TILC) is performed and the optimal input of the process can be obtained. Then, an initial disturbance compensation controller optimized by ILC is proposed. The results of the TNP control experiments demonstrate that the suggested control strategy can suppress the disturbances and achieve terminal pH control.

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“…Over the past few years, many academics have put forward control strategies, such as robust control [15,16], feedforward control [17], adaptive control [18][19][20], fuzzy control [21][22][23], optimal control [24,25], and so on. However, the control strategy proposed in aforementioned papers can only deal with systems whose dynamics are linearly characteristic.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Adaptive Generalized Predictive Control for PH Model of Nutrient Solution in Plant Factory Based on ANFIS

et al. 2023

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A plant factory is typically considered to be an exceedingly advanced product management system characterized by higher crop yields and better quality control. The pH value of the nutrient solution is crucial for determining the health and productivity of crops. However, the nutrient solution process exhibits inherent complexity, such as parameters uncertainty, multi-disturbances, and strong nonlinearity. Therefore, the traditional control method cannot meet the necessary requirements. The main objective of this paper is to address the issues of parameter uncertainty, strong nonlinearity, and multiple disturbances in the regulation process of the nutrient solution while achieving accurate control of the nutrient solution pH in a plant factory. This is performed so that a dynamic model of a nutrient solution for pH is developed and a nonlinear adaptive controller is presented, which comprises a linear adaptive generalized predictive controller, a nonlinear adaptive generalized predictive controller, and a switching mechanism. The parameters of the controller are adjusted by generalized predictive control (GPC) laws. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the unmodeled dynamics to depress the influence of nonlinearity on the system. The experiments show that the mean errors and standard errors for gain-scheduling the proportional-integral-derivative (PID) control strategy are 0.1388 and 0.4784, respectively. The mean errors and standard errors for the nonlinear adaptive controller are 0.1046 and 0.3009, respectively. Simulation results indicate that the presented method can acquire a better control effect in the case of various complex situations. Therefore, by achieving precise control of the pH value, it is possible to provide a suitable growth environment for crops, promoting healthy crop growth and increasing crop yield.

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A predictive control based on decentralized fuzzy inference for a pH neutralization process

Cited by 9 publications

References 35 publications

An Improved Method of Model-Free Adaptive Predictive Control: A Case of pH Neutralization in WWTP

An Improved Method of Model-Free Adaptive Predictive Control: A Case of pH Neutralization in WWTP

Iterative Learning Control for Tricalcium Neutralization Process with Initial Disturbance

Nonlinear Adaptive Generalized Predictive Control for PH Model of Nutrient Solution in Plant Factory Based on ANFIS

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