scholarly journals Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 610 ◽  
Author(s):  
Abhinav Garg ◽  
Hassan A. Abdulhussain ◽  
Prashant Mhaskar ◽  
Michael R. Thompson
Keyword(s):  
Model Predictive Control ◽  
Product Quality ◽  
Dynamic Model ◽  
Predictive Control ◽  
Raw Material ◽  
Data Driven ◽  
Rotational Molding ◽  
Molding Process ◽  
Internal Mold ◽  
Raw Material Variability

This work addresses the problems of uniquely specifying and robustly achieving user-specified product quality in a complex industrial batch process, which has been demonstrated using a lab-scale uni-axial rotational molding process. In particular, a data-driven modeling and control framework is developed that is able to reject raw material variation and achieve product quality which is specified through constraints on quality variables. To this end, a subspace state-space model of the rotational molding process is first identified from historical data generated in the lab. This dynamic model predicts the evolution of the internal mold temperature for a given set of input move trajectory (heater and compressed air profiles). Further, this dynamic model is augmented with a linear least-squares based quality model, which relates its terminal (states) prediction with key quality variables. For the lab-scale process, the chosen quality variables are sinkhole area, ultrasonic spectra amplitude, impact energy and shear viscosity. The complete model is then deployed within a model-based control scheme that facilitates specifying on-spec products via limits on the quality variables. Further, this framework is demonstrated to be capable of rejecting raw material variability to achieve the desired specifications. To replicate raw material variability observed in practice, in this work, the raw material is obtained by blending the matrix resin with a resin of slightly different viscosity at varying weight fractions. Results obtained from experimental studies demonstrate the capability of the proposed model predictive control (MPC) in meeting process specifications and rejecting raw material variability.

Model predictive control of uni-axial rotational molding process

2019 ◽  
Vol 121 ◽  
pp. 306-316 ◽  
Author(s):  
Abhinav Garg ◽  
Felipe P.C. Gomes ◽  
Prashant Mhaskar ◽  
Michael R. Thompson
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Rotational Molding ◽  
Molding Process

Bilinear Model Predictive Control of Plasma Keyhole Pipe Welding Process

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Kun Qian ◽  
YuMing Zhang
Keyword(s):  
Model Predictive Control ◽  
Dynamic Model ◽  
Predictive Control ◽  
Search Algorithm ◽  
Welding Process ◽  
Nonlinear Process ◽  
Continuous Change ◽  
Bilinear Model ◽  
Peak Current ◽  
Pipe Welding

Controlled quasi-keyhole plasma arc welding process adjusts the amperage of the peak current to establish a keyhole in a desired time. This keyhole establishment time is the major parameter that controls the consistence of the weld penetration/quality and needs to be accurately controlled. This paper addresses the control of keyhole establishment time during pipe welding around the circumference, in which the gravitational force acting on the weld pool continuously changes. Because of this continuous change, the dynamic model of the controlled process, with the keyhole establishment time as the output and the amperage of the peak current as the input, varies around the circumference during welding. In addition, it is found that this dynamic model is nonlinear. To control this time varying nonlinear process, the authors propose an adaptive bilinear model predictive control (MPC) algorithm. A self-search algorithm is proposed to decouple the input and output in the model to apply the proposed MPC. Experiments confirmed the effectiveness of the developed control system including the adaptive bilinear MPC.

Data Driven Model Predictive Control Design to Enhance Tracking Accuracy and Power Quality of a Solar Based Microgrid under Uncertain Load

2021 ◽  
Author(s):  
Merina Islam ◽  
Sumaiya Yeasmin ◽  
Shahriar Fahim ◽  
Subrata Sarker ◽  
Faisal Badal ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Power Quality ◽  
Predictive Control ◽  
Control Design ◽  
Data Driven ◽  
Tracking Accuracy

Data-driven model predictive control for building climate control: Three case studies on different buildings

2019 ◽  
Vol 160 ◽  
pp. 106204 ◽  
Author(s):  
Jiangyu Wang ◽  
Shuai Li ◽  
Huanxin Chen ◽  
Yue Yuan ◽  
Yao Huang
Keyword(s):  
Model Predictive Control ◽  
Case Studies ◽  
Predictive Control ◽  
Data Driven ◽  
Climate Control

scholarly journals Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting

2019 ◽  
Author(s):  
Ioanna Mitsioni ◽  
Yiannis Karayiannidis ◽  
Johannes A. Stork ◽  
Danica Kragic
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Data Driven

scholarly journals Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 237 ◽  
Author(s):  
Silvio Simani ◽  
Stefano Alvisi ◽  
Mauro Venturini
Keyword(s):  
Renewable Energy ◽  
Model Predictive Control ◽  
Energy Conversion ◽  
Wind Turbine ◽  
Predictive Control ◽  
Control Strategies ◽  
Data Driven ◽  
Control Techniques ◽  
Energy Conversion Systems ◽  
Hydroelectric Plants

The interest in the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this end, data-driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes of working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Therefore, the paper aims at providing some guidelines on the design and the application of different data-driven control strategies to a wind turbine benchmark and a hydroelectric simulator. They rely on self-tuning PID, fuzzy logic, adaptive and model predictive control methodologies. Some of the considered methods, such as fuzzy and adaptive controllers, were successfully verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some details of the implementation of the proposed control strategies to these energy conversion systems. The simulations will highlight that the fuzzy regulators are able to provide good tracking capabilities, which are outperformed by adaptive and model predictive control schemes. The working conditions of the considered processes will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many plants.

On the Use of Difference of Log-Sum-Exp Neural Networks to Solve Data-Driven Model Predictive Control Tracking Problems

2021 ◽  
Vol 5 (4) ◽  
pp. 1267-1272
Author(s):  
Sven Bruggemann ◽  
Corrado Possieri
Keyword(s):  
Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Data Driven
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