Bilinear Model Predictive Control of Plasma Keyhole Pipe Welding Process

Author(s):  
Kun Qian ◽  
YuMing Zhang

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.

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1551
Author(s):  
Federico Alberto Gorrini ◽  
Jesús Miguel Zamudio Lara ◽  
Silvina Inés Biagiola ◽  
José Luis Figueroa ◽  
Héctor Hernández Escoto ◽  
...  

In this study, the parameters of a dynamic model of cultures of the microalgae Scenedesmus obliquus are estimated from datasets collected in batch photobioreactors operated with various initial conditions and light illumination conditions. Measurements of biomass, nitrogen quota, bulk substrate concentration, as well as chlorophyll concentration are achieved, which allow the determination of parameters with satisfactory confidence intervals and model cross-validation against independent data. The dynamic model is then used as a predictor in a nonlinear model predictive control strategy where the dilution rate and the incident light intensity are simultaneously manipulated in order to optimize the cumulated algal biomass production.


2011 ◽  
Vol 4 ◽  
pp. 2620-2627 ◽  
Author(s):  
Katrin Prölß ◽  
Hubertus Tummescheit ◽  
Stéphane Velut ◽  
Johan Åkesson

Author(s):  
Zakariah Yusuf ◽  
Norhaliza Abdul Wahab ◽  
Abdallah Abusam

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux.


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