TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES

In this paper, a sensorless speed and armature resistance and temperature\nestimator for Brushed (B) DC machines is proposed, based on a Cascade-Forward\nNeural Network (CFNN) and Quasi-Newton BFGS backpropagation (BP). Since we wish\nto avoid the use of a thermal sensor, a thermal model is needed to estimate the\ntemperature of the BDC machine. Previous studies propose either non-intelligent\nestimators which depend on the model, such as the Extended Kalman Filter (EKF)\nand Luenberger's observer, or estimators which do not estimate the speed,\ntemperature and resistance simultaneously. The proposed method has been\nverified both by simulation and by comparison with the simulation results\navailable in the literature\n

Estimation of speed, armature temperature, and resistance in brushed DC machines using a CFNN based on BFGS BP