Performances Comparison of Neural Architectures for On-Line Speed Estimation in Sensorless IM Drives
نویسندگان
چکیده
The performance of sensor-less controlled induction motor drive depends on the accuracy of the estimated speed. Conventional estimation techniques being mathematically complex require more execution time resulting in poor dynamic response. The nonlinear mapping capability and powerful learning algorithms of neural network provides a promising alternative for on-line speed estimation. The on-line speed estimator requires the NN model to be accurate, simpler in design, structurally compact and computationally less complex to ensure faster execution and effective control in real time implementation. This in turn to a large extent depends on the type of Neural Architecture. This paper investigates three types of neural architectures for on-line speed estimation and their performance is compared in terms of accuracy, structural compactness, computational complexity and execution time. The suitable neural architecture for on-line speed estimation is identified and the promising results obtained are presented. Keywords—Sensorless IM drives, rotor speed estimators, artificial neural network, feedforward architecture, single neuron cascaded architecture.
منابع مشابه
IS-MRAS With On-Line Adaptation Parameters Based on Type-2 Fuzzy LOGIC for Sensorless Control of IM
This paper suggests novel sensorless speed estimation for an induction motor (IM) based on a stator current model reference adaptive system (IS-MRAS) scheme. The IS-MRAS scheme uses the error between the reference and estimated stator current vectors and the rotor speed. Observing rotor flux and the speed estimating using the conventional MRAS technique is confronted with certain problems relat...
متن کاملSpeed Observer Design for Linear Induction Motor Drives
In this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (LIMs). Dynamical equations of LIM have been considered accurate. In other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. Then equations of the reference model and adaptive mode...
متن کاملAn Mras Based Speed Estimation Method with a Linear Neuron for High Performance Induction Motor Drives and Its Experimentation
1This paper presents a new speed observer for high performance FOC (Field Oriented Control) and DTC (Direct Torque Control) induction motor drives. It is an MRAS (Model Reference Adaptive Systems) observer which employs a linear ANN (Artificial Neural Network) for the estimation of both the rotor speed and the flux-linkage. The training of the ANN based adaptive model of the MRAS observer is pe...
متن کاملA Current-Based Output Feedback Sliding Mode Control for Speed Sensorless Induction Machine Drive Using Adaptive Sliding Mode Flux Observer
This paper presents a new adaptive Sliding-Mode flux observer for speed sensorless and rotor flux control of three-phase induction motor (IM) drives. The motor drive is supplied by a three-level space vector modulation (SVM) inverter. Considering the three-phase IM Equations in a stator stationary two axis reference frame, using the partial feedback linearization control and Sliding-Mode (SM) c...
متن کاملA Novel MRAS Based Estimator for Speed-Sensorless Induction Motor Drive
In this paper, a novel stator current based Model Reference Adaptive System (MRAS) estimator for speed estimation in the speed-sensorless vector controlled induction motor drives is presented. In the proposed MRAS estimator, measured stator current of the induction motor is considered as a reference model. The estimated stator current is produced in an adjustable model to compare with the measu...
متن کامل