Multi-Network-Feedback-Error-Learning in pelletizing plant control

Ribeiro, Paulo Rogério de Almeida; Neto, Areolino de Almeida; Oliveira, Alexandre César Muniz de

doi:10.1109/icacc.2010.5486658

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The proposed controller using the FEL method [83] successfully reduces the seismic response of structures under dynamic uncertainties and estimation errors. This is accomplished by iteratively modifying the compensator using immediate feedback, hence enhancing the precision and agility of the control system [84]. The proposed controller demonstrates the capability to effectively handle a greater range of uncertainties compared to the traditional FOPID controller.…”

Section: Introductionmentioning

confidence: 99%

Design of a novel intelligent adaptive fractional-order proportional-integral-derivative controller for mitigation of seismic vibrations of a building equipped with an active tuned mass damper

Jafarzadeh,

Sabetahd,

Mousavi Ghasemi

et al. 2024

Smart Mater. Struct.

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The primary objective of this study is to introduce a novel adaptive fractional order proportional–integral–derivative (FOPID) controller. The adaptive FOPID controller’s parameters are dynamically adjusted in real-time using five distinct multilayer perceptron neural networks. The extended Kalman filter (EKF) is employed to facilitate the parameter-tuning process. A multilayer perceptron neural network, trained using the error Backpropagation algorithm, is employed to identify the structural system and estimate the plant. The real-time estimated Jacobian is applied to the controller to control the model. The stability and robustness of the adaptive interval type-2 fuzzy neural networks controller are enhanced by utilizing the EKF and the feedback error learning strategy for compensator tuning. This improvement increases resilience against estimation errors, seismic disturbances, and unknown nonlinear functions. The primary objective is to address the challenges posed by maximum displacement, acceleration, and drift, as well as the uncertainties arising from variations in stiffness and mass. In order to validate the reliability of the proposed controller, the performance investigation is carried out on an 11-story building equipped with an active tuned mass damper under far and near-field earthquakes. Numerical findings show the remarkable effectiveness of the proposed controllers compared to their predecessors. In addition, it is revealed that the inclusion of the adaptive interval type-2 fuzzy neural networks compensator has increased the performance of the proposed controller and shows significant capabilities in reducing the seismic responses of structures during severe earthquake events.

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Section: Introductionmentioning

confidence: 99%

Design of a novel intelligent adaptive fractional-order proportional-integral-derivative controller for mitigation of seismic vibrations of a building equipped with an active tuned mass damper

Jafarzadeh,

Sabetahd,

Mousavi Ghasemi

et al. 2024

Smart Mater. Struct.

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“…It is the future direction of pellet production control. 1) At present, most researches on intelligent control of grate-kiln pellet production focus on historical data based method [2][3][4][5] instead of empirical knowledge of domain experts, and reports of production application are rarely seen.…”

Section: Introductionmentioning

confidence: 99%

Expert System for Control Guidance of Grate-Kiln Pellet Production

2013

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The characteristics of large-scale equipments, continuous process, close system and severe environment make grate-kiln process hard for parameter detection and difficult to control. The accuracy and normalization of grate-kiln process control need to be improved. Therefore, an expert system providing real time control guidance for grate-kiln pellet production is presented. The "roasting temperature of pellet in kiln & gas temperature in gas hood of preheating section" dominated control strategy is put forward and control rules for grate-kiln process are established based on expertise. A multistage reasoning strategy of "parameter status estimation -reason analysis -adjustment measure selection" is developed and forward reasoning is adopted. The application of software, which is programmed with Visual C++, is outlined. The results show that, with the assistance of expert system, stability rate of grate-kiln pellet production is increased from 91.0% to 94.2%, FeO content of finished pellet is 0.05% lower, compressive strength is 86 N.pellet -1 higher, first grade rate of pellet is increased by 2.54%.

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