Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique

Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy partition. The δ rule, which is a basic learning method in neural networks, is used for parameter identification of a fuzzy model. SOFIA consists of four stages which effectively realize structure identification and parameter identification. The procedure of SOFIA is concretely demonstrated by a simple example which has been used in some modeling exercises. The identification result shows effectiveness of SOFIA. Next, the authors apply SOFIA to a prediction problem for CO concentration in the air at the busiest traffic intersection in a large city of Japan. Prediction results show that the fuzzy model is much better than a linear model. Furthermore, the authors simulate a control system for keeping CO concentration at a constant level by using the identified fuzzy model. A self-learning method for adaptively modifying controller parameters by δ rule is introduced because the dynamics of real CO concentration system changes gradually over a long period of time. Two self-learning controllers are designed in this simulation. One is a self-learning linear PI controller. The other is a self-learning fuzzy PI controller. The authors investigate robustness and adaptability of this control system for disturbance and parameter perturbation of the CO concentration model. Simulation results show that the self-learning fuzzy controller is more robust and adaptive