scholarly journals Experimental Study of Substrate Limitation and Light Acclimation in Cultures of the Microalgae Scenedesmus obliquus—Parameter Identification and Model Predictive Control

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 ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Dynamic Model ◽  
Predictive Control ◽  
Initial Conditions ◽  
Incident Light ◽  
Chlorophyll Concentration ◽  
Scenedesmus Obliquus ◽  
Light Illumination ◽  
Bulk Substrate ◽  
Determination Of Parameters

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.

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.

scholarly journals Dynamic model of a post-combustion absorption unit for use in a non-linear model predictive control scheme

2011 ◽  
Vol 4 ◽  
pp. 2620-2627 ◽  
Author(s):  
Katrin Prölß ◽  
Hubertus Tummescheit ◽  
Stéphane Velut ◽  
Johan Åkesson
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Dynamic Model ◽  
Predictive Control ◽  
Control Scheme ◽  
Non Linear

Fusion of inverse optimal and model predictive control strategies

2019 ◽  
Vol 42 (6) ◽  
pp. 1122-1134
Author(s):  
Lütfi Ulusoy ◽  
Müjde Güzelkaya ◽  
İbrahim Eksin
Keyword(s):  
Optimal Control ◽  
Lyapunov Function ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Initial Conditions ◽  
Big Bang ◽  
Receding Horizon ◽  
Control Lyapunov Function ◽  
Candidate Control ◽  
Function Matrix

In this study, model predictive control (MPC) and inverse optimal control (IOC) approaches are merged with each other and a new control strategy is evolved. The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications.

scholarly journals Dynamic Model Predictive Control

2008 ◽  
Vol 41 (2) ◽  
pp. 13182-13187
Author(s):  
Karl Mårtensson ◽  
Andreas Wernrud
Keyword(s):  
Model Predictive Control ◽  
Dynamic Model ◽  
Predictive Control

Research on Foot Trajectory Tracking of Parallel Wheel-legged Robot based on Dynamic Model Predictive Control

2020 ◽  
Author(s):  
Daohe Liu ◽  
Shoukun Wang ◽  
Zhihua Chen ◽  
Junzheng Wang
Keyword(s):  
Model Predictive Control ◽  
Dynamic Model ◽  
Predictive Control ◽  
Time Domain ◽  
Trajectory Tracking ◽  
Parallel Structure ◽  
Legged Robot ◽  
Response Frequency ◽  
Discrete State ◽  
Foot Trajectory

Abstract In this paper, the foot trajectory tracking control for parallel structure of the sixwheel-legged robot is investigated. The accuracy of trajectory tracking and dynamic responsewith heavy load are the main challenges of parallel mechanism. To guarantee the tracking performance and improve dynamic response frequency to posture input, a method based on dynamic model predictive control is proposed under the establishment of dynamic model of single leg. Newton-Eulerian equation is derived and converted into a discrete state space expression for velocity loop control, appropriate parameters including prediction time domain, control time domain and proportional gain are determined by co-simulation. Desired sinusoidal trajectories with different frequencies are tracked with satisfactory performance in terms of accuracy and response frequency. Finally, comparative experimental results using BIT-NAZA robot derived from the proposed control strategy indicate that the delay error and amplitude error are better than PI controller under the same conditions. This research can provide theoretical and engineering guidance for accurate planning of intelligent robot, and facilitate the control performance of wheel-legged robot in practical system.

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