Fuzzy Adaptive Type II Controller for Two-Mass System

Abstract

This paper presents original concepts of control systems for an electrical drive with an elastic mechanical coupling between the motor and the driven mechanism. The synthesis procedure of the speed controller uses a proposed quality index (cost function) of system operation ensures the minimization of both tracking errors and torsional vibrations. Proper selection of the cost function focusses more on the reduction of torsional vibrations due to their negative influence on the drive’s mechanical coupling vitality. The omission of the plant identification of an adaptive fuzzy controller was proposed. Two types of fuzzy controllers were analyzed, namely with type I and type II fuzzy membership functions. The novelty of the presented approach is in the application of a Petri transition layer in a type II fuzzy controller which reduces the numerical complexity in case of a large number of complicated type II fuzzy sets. The presented simulation and experimental results prove that the best dumping of mechanical vibrations ensures the adaptive fuzzy controller with type II functions and a Petri transition layer.

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

“…The second area of development involves the algorithms applied to the objects. The trends observed in the papers published in the journals and proceedings of conferences are focused on artificial intelligence [4][5][6]. A group of the applications is focused on neural networks [7][8][9][10][11].…”

“…Gradient methods are the most efficient for this purpose (3) and (4). Updates of network values are searched according to the assumption of finding the minimum value of the proposed objective function (5) and (6). Additionally, second-order training methods, based on cost function analysis, are used.…”

A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

“…In this paper, an LMI-bases approach is developed for robustness analysis of the designed type-2 FLC. The readers can find other methods for robustness analysis of Type-2 FLCs in [11,39,40].…”

“…Chen et al investigated the stability of TLP systems using Lyapunov's stability method and a fuzzy logic system (FLS), and designed a controller considering a constant delay [8,9]. Amini et al examined the control of TLP systems using linear-matrix inequalities (LMI) and a type-1 fuzzy controller considering uncertainty, delay and disturbance [10,11]. The adaptive FLSs are used in various uncertain problems [12,13]; however, for TLP, FLS-based controllers have been rarely designed.…”

Stable Rules Definition for Fuzzy TS Speed Controller Implemented for BLDC Motor

“…The second layer (inference) calculates conclusions and their significance in the final control signal through several iterations of the algorithm. Then, the received values are taken to the defuzzification layer and changed from the fuzzy variable to a specific value, which allows the fuzzy model to cooperate with the rest of the control system (e.g., the torque control loop of the electric drive) [34][35][36][37][38]. Additionally, the dynamic development of cheap and powerful microprocessor units makes it easier and more reasonable to use fuzzy logic controllers in everyday applications.…”