An Approach To Fuzzy Model-free Adaptive Coordinating Control

In regard to industrial processes whose variables are intimatelycorrelated, it is imperative to coordinate manipulated variables to ensuremain controlled variables keeping at desired values and the other onesrunning smoothly when impacted by immeasurable disturbances. In thiscontext, a fuzzy model-free adaptive coordinating control approach isexplicitly introduced. Taking advantage of model-free adaptive control(MFAC) metrics, deviations and deviation changes of measurements areutilized to accommodate fuzzy rules concerning alternative controlperformances of main and deputy controlled parameters which could beemployed to adaptively update the parameters of correspondingmodel-free adaptive controllers accordingly.Initially, model-free adaptive control (MFAC) strategies areintroduced along with their characteristics extensively studied by meansof simulations, which helps consider MFAC as the technical foundationof solving multivariable coordinating control problems. Subsequently, afuzzy MFAC based coordinating control approach which combines MFAC metrics and fuzzy coordinating rules is proposed. Simulationexperiments demonstrate the effectiveness of the contribution. Finally, theproposed approaches are applied to multivariable coordinating controlproblems consisting in a distillation process, simultaneously meeting thecontrol requirements of both main and deputy controlled variables underconsideration.It makes sense to expect that this contribution could serve as aneffective means responsible for coordinating control of industrial MIMOprocesses.