Stabilized adaptive controller based on direct IF–THEN knowledge of electronic systems for PWM drivers

Abstract

This article represents an adaptive control scheme which can be implemented to electric machines driven by pulse-width modulation techniques. The controller is constructed under the human knowledge related to the machine behavior such as IF we need to move motor more speed on clock wise direction THEN the duty cycle should be more than 50 %. The set of IF–THEN rules is directly implemented by an adaptive network called fuzzy rules emulated network (FREN). Furthermore, the controlled plant can be considered as a class of nonlinear discrete-time systems and the boundary of pseudo partial derivative can be estimated by plant’s input–output data set. Thus, the mathematical model of overall systems can be neglected for all designing phase and running phase. The adaptive algorithm is developed to tune all adjustable parameters inside FREN and the convergence and closed-loop stability can be guaranteed using the time varying learning rate. The experiment setup with lab-scale DC-motor and generator coupling demonstrates the validation of proposed controller.