A stator-flux-oriented vector-controlled induction motor drive with space-vector PWM and flux-vector synthesis by neural networks

A stator-flux-oriented vector-controlled induction motor drive is described where the space-vector pulsewidth modulation (SVM) and stator-flux-vector estimation are implemented by artificial neural networks (ANNs). ANNs, when implemented by dedicated hardware application-specific integrated circuit chips, provide extreme simplification and fast execution for control and feedback signal processing functions in high-performance AC drives. In the proposed project, a feedforward ANN-based SVM, operating at 20 kHz sampling frequency, generates symmetrical pulsewidth modulation (PWM) pulses in both undermodulation and overmodulation regions covering the range from DC (zero frequency) up to square-wave mode at 60 Hz. In addition, a programmable cascaded low-pass filter (PCLPF), that permits DC offset-free stator-flux-vector synthesis at very low frequency using the voltage model, has been implemented by a hybrid neural network which consists of a recurrent neural network (RNN) and a feedforward neural network (FFANN). The RNN-FFANN-based flux estimation is simple, permits faster implementation, and gives superior transient performance when compared with a standard digital-signal-processor-based PCLPF. A 5 HP open-loop volts/Hz-controlled drive incorporating the proposed ANN-based SVM and RNN-FFANN-based flux estimator was initially evaluated in the frequency range of 1.0-58 Hz to validate the performance of SVM and the flux estimator. Next, the complete 5 HP drive with stator-flux-oriented vector control was evaluated extensively using the PWM modulator and flux estimator