FUZZY-SYSTEM-BASED CORRECTION OF NONLINEARITIES IN COMPLEX DYNAMIC PLANTS

A method for synthesizing a fuzzy compensating element from the static characteristic of a nonlinear plant is presented. The method transforms the characteristic into a rotated coordinate system, identifies zero crossings and local extreme, maps the characteristic points back to the original coordinates, and uses them to determine the antecedent membership functions and singleton consequents of a zero-order Sugeno inference system. Unlike heuristic rule tuning, the proposed procedure derives the rule base directly from the geometry of the nonlinear characteristic. An adaptive reconfiguration mechanism based on a mismatch indicator is also formulated for operating conditions in which plant parameters vary over time. The method was evaluated using a sinusoidal test signal covering the complete input range and a four-point disturbance scenario in a warping-process control system. The root-mean-square mismatch between the input and output signals decreased from 3.8112 before correction to 0.0102641 after correction, corresponding to a 99.73% reduction. Under the disturbance scenario, the number of electric-drive switching actions over 100 s was reduced approximately twofold, while the transient overshoot remained about 2%. These results indicate that the proposed fuzzy compensator improves input-output conformity and reduces actuator activity without violating the adopted transient-quality constraint. The approach is applicable to nonlinear technological plants whose static characteristics can be experimentally identified and periodically updated.