A systematic approach for fine-tuning of fuzzy controllers applied to WWTPs
A systematic approach for fine-tuning fuzzy controllers has been developed and evaluated for an aeration control system implemented in a WWTP. The challenge with the application of fuzzy controllers to WWTPs is simply that they contain many parameters, which need to be adjusted for different WWTP applications. To this end, a methodology based on model simulations is used that employs three statistical methods: (i) Monte-Carlo procedure: to find proper initial conditions, (ii) Identifiability analysis: to find an identifiable parameter subset of the fuzzy controller and (iii) minimization algorithm: to fine-tune the identifiable parameter subset of the controller. Indeed, the initial location found by Monte-Carlo simulations provided better results than using trial and error approach when identifying parameters of the fuzzy controller. The identifiable subset was reduced to 4 parameters from a total of 33, which improved the quality of the optimization of the control system by a minimization algorithm. Overall the systematic approach considerably improved the performance of the control system as measured by the Integral Absolute Error (deviation between the set-point and the controlled variable) of the controllers. Moreover, the methodology overcomes the dependency of the initial parameter space issue typical of local identifiability analysis. All in all this systematic approach is expected to facilitate the design and application of fuzzy controllers in particular to WWTPs compared to the time-consuming and tedious trial and error approach currently used in practice.
identifiability calibration fuzzy logic controller latin hypercube sampling systematic fine-tuning nitrogen and phosphorus removal