Closed loop control of laser welding processes using cellular neural network cameras - Experimental results

2009 ◽  
Author(s):  
F. Abt ◽  
A. Blug ◽  
L. Nicolosi ◽  
F. Dausinger ◽  
R. Weber ◽  
...  
Keyword(s):  
Neural Network ◽  
Laser Welding ◽  
Closed Loop ◽  
Cellular Neural Network ◽  
Experimental Results ◽  
Closed Loop Control ◽  
Loop Control ◽  
Welding Processes

scholarly journals Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santiago Rómoli ◽  
Mario Serrano ◽  
Francisco Rossomando ◽  
Jorge Vega ◽  
Oscar Ortiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
Tracking Error ◽  
Closed Loop Control ◽  
Online Information ◽  
Fed Batch ◽  
Loop Control ◽  
Batch Bioreactor

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

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