Intelligent adaptive control of nonlinear dynamical systems with a hybrid neuro-fuzzy-genetic approach

We describe different hybrid approaches for controlling dynamical systems in electrochemical applications. The hybrid approaches combine soft computing techniques and mathematical models to achieve the goal of controlling the electrochemical process to follow a desired production plan. We develop several hybrid architectures that combine fuzzy logic, neural networks, and genetic algorithms, 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 charging, are very complex and for this reason very difficult to control. We achieved very good results using the fuzzy logic for control, neural networks for modelling the process, and genetic algorithms for tuning the hybrid intelligent system