this paper presents fuzzy and conventional performance of model reference adaptive control(mrac) to control a dc drive. the aims of this work are achieving better match of motor speed with reference speed, decrease of noises under load changes and disturbances, and increase of system stability. the operation of nonadaptive control and the model reference of fuzzy and conventional adaptive contr...

In this paper, we present CAC, a new configurable, generic and adaptive algorithm for establishing arc consistency for binary constraints. CAC is configurable, that is by combining some parameters CAC corresponds to any existing AC algorithm: AC-3, AC-4, AC-6, AC-7, AC-2000, AC-2001, AC-8, AC-3d, AC-3.2 and AC-3.3. CAC is generic, like AC-5, because it may takes into account the structure of th...

One of the most critical operations aspects is power management strategies for hybrid AC/DC microgrids. This work presented in AC–DC microgrids with a droop control strategy. At first, photovoltaic, Wind, and battery are used as sources, which supply uncertainties. The AC DC controlled by an Adaptive neuro-fuzzy inference system (ANFIS) controller Proportional Integral Derivative (PID) controll...

From a control viewpoint, DC motors must be maintained frequently due to their brushes and rectifiers. Sometimes the motor is installed in a location that makes it difficult or impossible to maintain or repair the motor. Compared to the disadvantages of DC motors, AC motors have a variety of advantages. for example, AC motors feature small size, light weight, low rotary inertia, and low price. ...

This paper proposes a distributed control strategy that considers several source characteristics to achieve reliable and efficient operation of a hybrid ac/dc microgrid. The proposed control strategy has a two-level structure. The primary control layer is based on an adaptive droop method, which allows local controllers to operate autonomously and flexibly during disturbances such as fault, loa...

an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...