Dynamic model prediction control for an activated sludge model based on a T-S multi-model

2015 ◽  
Author(s):  
Matoug Lamia ◽  
Khadir M. Tarek
Keyword(s):  
Activated Sludge ◽  
Dynamic Model ◽  
Model Prediction ◽  
Activated Sludge Model ◽  
Model Based ◽  
Model Prediction Control ◽  
Prediction Control

Optimizing the Step-Feed Ratio and Modeling the Effluents of Aerobic Tank with ASM1 in ECOSUNIDE Process

2012 ◽  
Vol 178-181 ◽  
pp. 526-530
Author(s):  
Ruo Gu Li ◽  
Yan Qiu Zhang
Keyword(s):  
Organic Matter ◽  
Activated Sludge ◽  
Ammonia Nitrogen ◽  
Feed Ratio ◽  
Optimum Ratio ◽  
Matlab Simulink ◽  
Activated Sludge Model ◽  
Model Based ◽  
Autotrophic Bacteria ◽  
Optimum Model

The step feed model based on the Activated Sludge Model No.1 (ASM1) and the optimum model of the ammonia nitrogen (SNH) removal in wastewater were established. Four aeration tanks under the different step feed ratios were simulated by Matlab Simulink. The results show that single-feeding is conducive to the removal of readily biodegradable substrate (SS) and the growth of heterotrophic organisms (XBH), and to lower the biodegradable substrate (XS) at the same time. The SS, XS, and SNH concentrations are 1.36, 5.98, and 3.02 mg/L respectively in effluent. However, the step-feeding is conducive to the SNH removal, and the autotrophic bacteria (XBA) growth. Under the step feed ratio (25/25/25/25%), the SS, XS, and SNH concentrations are 2.64, 10.79, and 2.61 mg/L respectively. Under the optimum ratio (28.7/23.6/20.4/27.2%), step-feeding could further facilitate the removal of SNH and hinder the removal of organic matter, their concentrations are 2.70, 10.98, and 2.47 mg/L respectively.

An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle

2019 ◽  
Vol 233 (12) ◽  
pp. 3246-3262 ◽  
Author(s):  
Bing Zhang ◽  
Changfu Zong ◽  
Guoying Chen ◽  
Guiyuan Li
Keyword(s):  
Electric Vehicle ◽  
Model Prediction ◽  
Hierarchical Control ◽  
Path Tracking ◽  
Vehicle Dynamic ◽  
Independent Control ◽  
Prediction Horizon ◽  
Adaptive Prediction ◽  
Model Prediction Control ◽  
Prediction Control

An adaptive-prediction-horizon model prediction control-based path tracking controller for a four-wheel independent control electric vehicle is designed. Unlike traditional model prediction control with fixed prediction horizon, this paper devotes to satisfy the varied path tracking demand by adjusting online the prediction horizon of model prediction control according to its effect on vehicle dynamic characteristics. Vehicle dynamic stability quantized with the vehicle sideslip-feature phase plane is preferentially considered in the prediction horizon adjustment. For stability during switching prediction horizon and for robustness during path tracking, the numerical problem inherent in the adaptive-prediction-horizon model prediction control is analysed and solved by introducing exponentially decreasing weight. Subsequently, the desired motion for path tracking with the four-wheel independent control electric vehicle is realized with a hierarchical control structure. Simulation results finally illustrate the effectiveness of the proposed method.

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