퍼지 PID 제어기의 성능개선과 비선형 유압실린더 위치제어에의 응용

Abstract

In order to derive a new fuzzy controller, a fixed design-parameter fuzzy PID controller was surveyed in view of deriving a control law based on the design procedure of general fuzzy logic controllers. The controller is known to be very useful to control unknown nonlinear systems because it has nonlinear time-varying PID controller gains. While it has advantages that the resultant form of the controller is an analytical mathematical form and fuzzy rules are so simple, it also has a disadvantage that the PID control action is not applied at all or cannot be accurately applied when operating inputs are greater than or smaller than the reference input used to decide design-parameters in initial controller design procedure.

In this paper, in order to improve the disadvantage of the fixed design-parameter fuzzy PID controller, a new fuzzy PID controller named a variable design-parameter fuzzy PID controller was suggested. The main characteristic of the controller is to adjust design-parameters of the controller by comparing magnitudes between fuzzy controller inputs at each sampling time when controller inputs are measured. As a result, all fuzzy input partitions converge within a time-varying normalization parameter and the resultant PID control action can always be applied precisely regardless of operating input magnitudes.

In order to verify the effectiveness of the suggested controller, several computer simulations were executed for example mathematical systems such as a linear system and a nonlinear system. And another simulations were executed for a hydraulic position control system which is one of the typical nonlinear systems in the real field. The output of the suggested control system was compared with the outputs of linear PID and the fixed design-parameter fuzzy PID control system for several reference inputs.

In the conclusion, the variable design-parameter fuzzy PID controller was assured to be a very effective and useful method to control unknown nonlinear systems from the simulation results.