On designing optimal control systems through genetic and neuro-fuzzy techniques

Many industrial processes are affected by flow disturbances and sensor noise. To maintain optimal timing performances, the control system needs to adapt continuously to these changes. The goodness of a control system depends on timing parameters such as settling time, rise time and overshoot. Avoiding undesirable overshoot, longer settling times and vibration from a state to another one, the designed control system gives optimal control performances. Control problems can be overcome using computational intelligence procedures. The target of this work is to find optimal combinations of intelligent techniques such as fuzzy logic, Genetic Algorithms and neural networks to obtain good control performances. The membership functions of the designed fuzzy controllers are optimized through Genetic Algorithms. Moreover, the fuzzy rules weights are tuned both Genetic Algorithms and neural networks. In this way, the control system has the capability to learn from data. The results show that our controllers improve the timing performances of conventional controllers. Moreover, the fuzzy rules weights optimization with Genetic Algorithms is improved using neural networks techniques which suitably tune the weights.