Design and control of a novel Eddy current dynamometer

The recent studies on Artificial Intelligence accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) and Proportional-Integrative-Differential (PID) control techniques are widely used. They are safe and reliable control method mainly based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive Model Predictive Control and PID control that utilizes selected AI methods. The presented approach presents an updated the mathematical model of an Eddy Current dynamometer based on experimentally obtained system operational data. Moreover, the proposed PID control system proves changeable Kp (Proportional gain), Ki, (Integral gain) and Kd (Derivative gain) parameters and MPC with continuously-updated the mathematical model of an Eddy Current Dynamometer in real-time to adapt and presents a good capacity to adapt nonlinearities and bring robustness. Finally, the comparison of AI methods and related learning performances based on the assessment technique of mean absolute percentage error (MAPE) issues are discussed. The results indicate that basic feed-forward Neural Network, General Regression Neural Network (GRNN), Radial Basis Network (RB), Neuro Fuzzy Network (ANFIS) coupled MPC and PID have quite satisfying performances.