Controlling Electrochemical Processes
We describe in this chapter, different hybrid approaches for controlling dynamical systems in manufacturing applications. The hybrid approaches combine soft computing techniques and mathematical models to achieve the goal of controlling the manufacturing process to follow a desired production plan. We have developed several hybrid architectures that combine fuzzy logic, neural networks, and genetic algorithms, to compare the performance of each of these combinations and decide on the best one for our purpose. Electrochemical processes, like the ones used in battery formation, are very complex and for this reason very difficult to control. We have achieved very good results using fuzzy logic for control, neural networks for modelling the process, and genetic algorithms for tuning the hybrid intelligent system. For this reason, we consider that the neuro-fuzzy-genetic approach is the most appropriate for this case.
KeywordsGenetic Algorithm Membership Function Fuzzy Logic Fuzzy System Fuzzy Rule
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