The Stirling engine is a type of external heat engine that can convert renewable energy forms to electricity or mechanical rotation. Therefore, monitoring and controlling some of its parameters such as its speed is a demand. The purpose of this paper is to control a Stirling engine flywheel rotation speed using an intelligent parameter predictor for controlling purposes. Since various parameters affect motor performance, manually determining of the optimal value is impossible. Therefore, linking these parameters together and finding a relationship between them would be beneficial. Hence, using artificial intelligence to find a quick and efficient solution is particularly important. At the studied Stirling engine, rotation speed, cold sink, and ambient temperatures are defined as input parameters, and hot sink temperature is considered as the output parameter that should be calculated. To discover the relationship between inputs and output, an artificial neural network (ANN) is used. The results of this study showed that the use of ANNs can be significantly helpful in controlling engine flywheel rotation speed.
