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AC asynchronous motors are the most frequent motors used in industrial motion control systems as well as in main powered domestic appliances. AC asynchronous motors provide several advantages, including a simple and durable construction, low cost, low maintenance, and direct connection to an AC power source. On the market, there are many different types of AC asynchronous motors. Diverse motors are suitable for various applications. Like other motors, an AC asynchronous motor contains a stator that is fixed on the outside and a rotor that spins within, separated by a precisely designed air gap. Almost all electric motors use magnetic field rotation to spin their rotors. Asynchronous motors are the most commonly utilized electromechanical machinery in industrial applications. There have been several research on their control, as well as a variety of approaches for achieving high-performance speed drivers. The properties of asynchronous motors alter with time and under different operating situations. Even though much research has gone into developing artificial neural network techniques to estimate the speed of an asynchronous motor and some of the parameters such as flux and torque, there hasn’t been much work done on asynchronous motor speed estimation using artificial neural networks. The main purpose of this paper is to build a speed estimator for an asynchronous motor using a feed-forward artificial neural network. A mathematical model for the asynchronous motor is developed and implemented in MATLAB. A speed estimator is then developed using a feed-forward neural network and coupled to the asynchronous motor to estimate its speed. After that, simulations are done to see how well the proposed speed estimator performed.
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