Distributed Model Predictive Control of Iron Precipitation Process by Goethite Based on Dual Iterative Method

Chen, Ning; Dai, Jiayang; Zhou, Xiaojun; Yang, Qingqing; Gui, Weihua

doi:10.1007/s12555-017-0742-6

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…According to literature [4], normally the value of reaction rate constants    ，， can be 1. In this paper, therefore, the value of    ，， is set 1.…”

Section: Description Of the Optimal Control Of Iron-removal Systemmentioning

confidence: 99%

“…For the non-linear problems in the industrial production process, a lot of effective control methods have been proposed [3][4][5][6][7][8][9][10]. According to the law of mass conservation and reaction kinetics, Chen [4] et al established a single-tank continuous stirred reactor model based on the law of conservation of mass and reaction kinetics for the first time. Based on the single-reactor model, considering the existence of unreacted oxygen in the leaching solution, a cascade reactor coupled control distributed model is established.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Control of Iron-Removal Systems Based on Off-Policy Reinforcement Learning

et al. 2020

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“…According to literature [4], normally the value of reaction rate constants    ，， can be 1. In this paper, therefore, the value of    ，， is set 1.…”

Section: Description Of the Optimal Control Of Iron-removal Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Control of Iron-Removal Systems Based on Off-Policy Reinforcement Learning

et al. 2020

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“…then controller (5) stabilizes system (6) satisfying physical constraints (3) and (4), where the controller gain K � KQ − 1 .…”

Section: Theorem 1 If There Exist a Scalar λ > 0 And Matricesmentioning

confidence: 99%

“…Receding horizon control (RHC) is known for the capacity to deal with the multivariable constraint by the receding horizon optimization. It has obtained a lot of attention in academic [1][2][3][4][5][6], and the control policy has been applied to many actual systems such as multiagent systems [7], chemical engineering [8], and hybrid electric vehicles [9].…”

Section: Introductionmentioning

confidence: 99%

Robust Receding Horizon Control with Free Control Moves for Polytopic Parametric Uncertainty Systems Subject to Multiple Input Delays

Yang

Cai

Ding

2020

Mathematical Problems in Engineering

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A robust receding horizon control (RHC) with free control moves is applied to polytopic parametric uncertainty systems with multiple input delays and unstable system matrices. A difficulty in the previous robust RHC work is that the free control moves are unsuitable for the system with input time delay, which is overcome in this paper by the design based on the augmented state. As a result, the synthesis of local control based on augmented feedback is given offline to alleviate the online computation burden. The free control moves before the augmented feedback are the online decision variables, which are solved by minimizing a sequence of nonnegative scalars online. The recursive feasibility is guaranteed by adopting the augmented state space equation. By adjusting the robust positively invariant set, the stability of the closed-loop system is guaranteed. Simulation results demonstrate that the proposed algorithm improves the control performance effectively.

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“…The difficulty of modeling goethite process lays in its complicate mechanism, the process has random uncertainties of dynamic data and information. In addition, the goethite process has large time-lag [2,3,4,15]. Fuzzy Cognitive Map (FCM) [12] is an intelligent method can deal with the dynamic data and information with stochastic uncertainty.…”

mentioning

confidence: 99%

A goethite process modeling method by Asynchronous Fuzzy Cognitive Network based on an improved constrained chicken swarm optimization algorithm

Peng¹,

Chen²,

Dai³

et al. 2021

Journal of Industrial &Amp; Management Optimization

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In order to solve the problem that the mechanism model of nonlinear system with uncertainty is difficult to establish, a modeling method of nonlinear system based on Asynchronous Fuzzy Cognitive Network (AFCN) is proposed. This method combines fuzzy cognitive network with time-lag system, and extends the node state values and weights of fuzzy cognitive network to the time interval, which enhances the adaptability of the model. At the same time an improved constrained chicken swarm optimization algorithm(ICCSOA) is proposed to identify model parameters of AFCN. A lag matrix corresponding to the actual measured values of the system lag of the nodes in the AFCN model is introduced, and a correction term including the difference between the measured values and the predicted values of the system is added to the model parameter updating mechanism. The simulation experiment results of goethite process system shows this modeling method can be used to model complex systems with uncertainties or partial missing data. The control model based on the established system model can make correct control decisions. ICCSOA has the advantages of fast convergence speed and accurate learning results, whose global search ability and convergence accuracy are higher than those of CSO algorithm, which can be widely used to the modeling of uncertain systems.

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Distributed Model Predictive Control of Iron Precipitation Process by Goethite Based on Dual Iterative Method

Cited by 13 publications

References 32 publications

Optimal Control of Iron-Removal Systems Based on Off-Policy Reinforcement Learning

Optimal Control of Iron-Removal Systems Based on Off-Policy Reinforcement Learning

Robust Receding Horizon Control with Free Control Moves for Polytopic Parametric Uncertainty Systems Subject to Multiple Input Delays

A goethite process modeling method by Asynchronous Fuzzy Cognitive Network based on an improved constrained chicken swarm optimization algorithm

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