Experimental validation of predictor-corrector approach based control schemes on the laboratory scale non-linear system

In this work, the authors have designed and implemented predictor-corrector approach based control schemes for a single input-single output nonlinear system. The controller output is computed in two steps. The first step explicitly uses a non-linear model or multiple-linear models weighted using fuzzy membership function to compute the value of the controller output. The second step is based on the measurement, where the value of the controller output computed in first step is updated. The extensive simulation studies show that the set-point tracking and disturbance rejection capability of the proposed control schemes are found to be satisfactory in the absence and presence of Model-plant mismatch. The performances of the proposed control schemes have been compared with that of a gain-scheduled PI controller. In addition, the control schemes are experimentally validated on the laboratory scale conical tank experimental setup.