We present a randomized algorithm called Model Reference Adaptive Search (MRAS) for solving global optimization problems. The algorithm generates at each iteration a group of candidate solutions according to a parameterized probabilistic model. These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the future search will be bi...

We survey some randomized search methods for global optimization that are based on sampling from an underlying probability distribution “model” on the solution space. In this approach, the probability model is updated iteratively after evaluating the performance of the samples at each iteration. Such model-based methods include estimation of distribution algorithms (EDAs), the cross-entropy met...

This paper discusses a model reference adaptive sliding-mode control of the sensorless vector controlled induction motor drive in a wide speed range. The adaptive speed controller uses on-line trained fuzzy neural network, which enables very fast tracking of the changing speed reference signal. This adaptive sliding-mode neuro-fuzzy controller (ASNFC) is used as a speed controller in the direct...

This paper presents a different technique for the online stator resistance estimation using particle swarm optimization (PSO) based algorithm rotor flux oriented control schemes of induction motor drives without speed sensor. First, conventional proportional-integral controller-based is used sensorless scheme with two model reference adaptive system (MRAS) concepts. Finally, novel method on PSO...

To solve the problem of sensorless control method a permanent magnet synchronous motor, based on study mathematical model for motor and adaptation theory, reference equation adjustable are derived according to stator current equation. The correctness selected linear compensator matrix is strictly proved. Then, Popov’s super-stability theory used derive speed adaptive law prove its asymptotic st...

This paper aims to evaluate the dynamic performance of a five-phase PMSM drive using two different observers: sliding mode (SMO) and model reference adaptive system (MRAS). The design vector control for is firstly introduced in details visualize proper selection speed current controllers’ gains, then construction observers are presented. stability check also presented analyzed, finally evaluati...

To enhance the anti-inertia disturbance ability of permanent magnet synchronous motor (PMSM) speed system, an adaptive sliding mode control with inertia identification is proposed. A novel (NSMC) based on a new reaching law coupled model reference system (MRAS) realized control, named MRAS+NSMC. In NSMC construct process, integral surface and variable are introduced to avoid differentiation imp...

AC drives based on pulse width modulation (PWM) voltage source inverters (VSI’s) have many drawbacks, Especially for high performances applications. The inverter nonlinearities such as dead time and power devices voltage drop are one of the main reasons. In this paper a new method to estimate and compensate inverter nonlinearities is presented. The proposed method is based on Model Reference Ad...

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...

For an interior permanent magnet synchronous motor (IPMSM) control system, the actual maximum torque per ampere (MTPA) trajectory may deviate from the ideal one with parameter variances, and thus the system may not obtain the maximum torque output. In view of this problem, a novel parameter identification method based on the model reference adaptive system (MRAS) is proposed in this paper. In t...