Sliding Mode Control of Aerobic Bioprocess using Recurrent Neural Identifier
| Authors: | Baruch Ieroham, CINVESTAV-IPN, Mexico
Hernandez Luis-Alberto, CINVESTAV-IPN, Mexico
Valle Jesus-Roberto, CINVESTAV-IPN, Mexico
Barrera-Cortes Josefina, CINVESTAV-IPN, Mexico |
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| Topic: | 3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.) |
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| Session: | Adaptive Neuro-fuzzy Control |
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| Keywords: | sliding mode control, integral action, discrete-time systems, adaptive control, neural network models, backpropagation algorithms, identification, state estimation, biotechnology, aerobic continuous stirred tank reactor plant model |
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Abstract
The paper proposed a new adaptive control system containing a Recurrent Neural Network (RNN) identifier, a Sliding Mode Controller (SMC), and an integral term. The SMC is derived defining the sliding surface with respect to the output tracking error. The state and parameter information to resolve the SMC is obtained from a RNN identifier, which permit the SMC to maintain the sliding regime when the plant parameters changed. The simulation results obtained with a continuous stirred tank reactor plant model confirmed the good quality of the control.
