Control of Nonlinear Processes using Fuzzy logic and Neural Networks
Control of Nonlinear Processes with Input Multiplicities Using Fuzzy Logic and Neural Networks Control Techniques
Herausgegeben: Chinna Eranna, Ballekallu
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The book has been designed for Chemical Engineers.It is focused on Isothermal Continuous Stirred Tank Reactor (CSTR) and Continuous Bioreactor control has become an active area of research in recent years. This is partially attributable to the fact that CSTR and Bioreactors can be extremely difficult to control. Their dynamic behavior is invariably non-linear and model parameters vary in an unpredictable manner. Accurate process models are rarely available due to complexity of the underlying chemical and biochemical processes. Considerable emphasis has been placed on the control of CSTR and Bi...
The book has been designed for Chemical Engineers.It is focused on Isothermal Continuous Stirred Tank Reactor (CSTR) and Continuous Bioreactor control has become an active area of research in recent years. This is partially attributable to the fact that CSTR and Bioreactors can be extremely difficult to control. Their dynamic behavior is invariably non-linear and model parameters vary in an unpredictable manner. Accurate process models are rarely available due to complexity of the underlying chemical and biochemical processes. Considerable emphasis has been placed on the control of CSTR and Bioreactor because of their prevalence in industries. Model based nonlinear controllers for processes input multiplicities have been studied and they are computationally involved. The design of knowledge based Fuzzy logic controller and data based Direct Inverse Neural Network Controller are proposed to overcome the control problems of nonlinear processes due to input Multiplicities. Among twoadvanced controllers proposed in this research work the direct inverse neural network control is faster with with some offset than fuzzy logic controller in all the results.
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