Sensorless MTPA Control for SynRM Based on Speed and Torque Observer

High performance induction motors (IM) require a robust and reliable speed controller to maintain the speed tracking performance under various uncertainties and disturbances. This paper presents a sensorless speed controller for IM based on speed and perturbation estimation and compensation. By defining a lumped perturbation term to include all unmodeled nonlinear dynamics and external disturbances, two state and perturbation observers are designed with combining the model reference adaptive system (MRAS) based speed observer to estimate the flux and speed states and the flux- and speed-loop related lumped perturbation terms. The estimated flux, speed and perturbation terms are used to design an output feedback, speed sensorless nonlinear adaptive controller (SSNAC) for IM. The stability of the closed-loop system is addressed in Lyapunov theory. Effectiveness of the SSNAC is verified via simulation and experiment tests. Comparing with the standard vector control plus MRAS speed observer (VC-MRAS), the proposed SSNAC reduces the speed tracking error by 20% to 30% on average under model uncertainties and unknown load disturbance due to the estimation and compensation of perturbation terms. The combined observer can estimate the real rotor speed under speed varying and load changes and thus makes SSNAC achieve high performance robust speed drive without using speed sensors.

In this paper, a novel sensorless control structure based on multi-scalar variables is proposed. The static feedback control law is obtained by using the multi-scalar variables transformation, where the multi-scalar variables approach allows a full linearization of the nonlinear system. The control system could be described as “optimized” because of the minimized number of controllers. Furthermore, control system is divided into two independent control systems which are synchronized by additional controllers. The first control system is responsible for the generation of the 1st fundamental harmonic, while the second control system is responsible for the generation of the 3rd harmonic. Both control systems are synchronized and ensure a trapezoidal shape of air gap induction waveform regardless of the machine operation point. In order to ensure a sensorless control system the rotor angular speed and the state variables are estimated by a speed observer. This speed observer is based on the backstepping approach. All analytical proposed assumptions are confirmed by simulations and experimental results.

This paper presents a discrete time torque observer and an extended H∞ filter to estimate the rotor speed and position in a permanent magnet machine. Those estimated variables are key components of a robust sensorless controller for the machine when it is associated with a wind turbine emulator test-rig. In addition, an iterative maximum power point tracking algorithm is proposed for maximum efficiency of the device. Experimental verification on the laboratory setup is given to demonstrate the correct integration of all the control algorithms.

This paper proposes and investigates an offline finite-element-method (FEM)-assisted position and speed observer for brushless dc permanent-magnet (PM) (BLDC-PM) motor drive sensorless control based on the line-to-line PM flux linkage estimation. The zero crossing of the line-to-line PM flux linkage occurs right in the middle of two commutation points (CPs) and is used as a basis for the position and speed observer. The position between CPs is obtained by comparing the estimated line-to-line PM flux with the FEM-calculated line-to-line PM flux. Even if the proposed observer relies on the fundamental model of the machine, a safe starting strategy under heavy load torque, called I-f control, is used, with seamless transition to the proposed sensorless control. The I-f starting method allows low-speed sensorless control, without knowing the initial position and without machine parameter identification. Digital simulations and experimental results are shown, demonstrating the reliability of the FEM-assisted position and speed observer for BLDC-PM motor sensorless control operation.

Observability measurement and control strategy for induction machine sensorless drive in traction applications

The Bearingless Switched Reluctance Motor (BSRM) is a new technology motor, which overcomes the problems of maintenances required associated with mechanical contacts and lubrication of rotor shaft effectively. In addition, it also improves the output power developed and rated speed. Hence, the BSRM can achieve high output power and super high speed with less size and cost. It has a considerable ripple in the net-torque due to its critical non-linearity and the salient pole structures of both stator and rotor poles. The resultant torque ripple, especially in these motors, causes the more vibrations and acoustic noises will affects the levitated rotor safety also. Practically at high-speed operations, the accurate measurement of the rotor position is complicated for conventional mechanical sensors. A new square currents control with global sliding mode control based sensorless torque observer is proposed to minimize the torque ripple and achieve a smooth, robust operation without using any mechanical sensors. The proposed controller is designed based on the error between the reference and measured torque values. The sliding mode torque observer measures the torque from the actual phase voltages, currents, and look-up tables. The simulation model has been modelled to validate the proposed methodology. From the simulation outputs, it is clear that the reduction of torque ripple by the proposed method shows improved than the conventional sliding mode controller. The overall system is more robust to the external disturbances, and it also gets efficient torque profile.

In this paper a new speed and disturbance torque observer, applied to the induction machine with encoder, is proposed. In practical situations, when designing the control loop, the noise statistics needed for tuning a stochastic observer are generally unknown. However, the bounds of the process and measurement noises can be clearly defined, for example, if they are generated by a quantizer. The increased DSP computational power and new mathematical tools, like the interval arithmetic, allow us to calculate both the bounds of the estimation error and the observed states. A nonlinear optimization algorithm is used here to obtain the gain of the observer that minimizes the estimation error. Some experimental tests with an AC motor have been performed in order to compare the proposed observer with a state bounding algorithm.

It is described a sensorless direct current motor drive. The speed was controlled using sliding mode with a first- order differential equation based on the speed error. The result of the switching function was integrated with a current limitation using a logic equation. The current limitation assures the machine protection and the output of the logic block is the signal of the voltage to apply on the motor armature. Instead to measure the rotor speed it was analytically inferred an external torque and speed observer. The motor is fed by a classic insulated gate bipolar transistor converter module. All gating signals for power switches are determined at every sample instant by a single microcontroller, which collects information and analyze voltage and current data from the motor to perform the necessary speed and torque estimation.

Speed sensorless control of a bearingless induction motor with combined neural network and fractional sliding mode

Sensorless efficient fault-tolerant control of wind turbines with geared generator

This paper first proposes a new benchmark for sensorless induction motor controllers validation under three operating conditions: (1) low speed with nominal load; (2) high speed with nominal load; (3) case where the motor state is unobservable (at low frequencies). The second part of the paper presents a sensorless field oriented control with sliding mode using an interconnected high gain observer which has been tested and validated on the reference trajectories of this benchmark.

In this paper, the authors are interested on the sensorless field-oriented control of doubly fed Induction Motor (DFIM) fed by two PWM inverters with separate DC bus link. By introducing a new approach for decoupling the motor's currents in a rotating (d-q) frame, based on the state space input-output decoupling method, we obtain the same transfer function (1/s) for all four decoupled currents. Thereafter we presented the Lữenberger speed observer, the estimated speed is used and feedback in a vector control system. The Results obtained in Matlab/Simulink environment show well the effectiveness of the technique employed for the decoupling and the estimated speed regulation of the machine.

In this paper, a modified Kalman filter is proposed to estimate speed. At first, the influence of the magnetic saturation is taken into account in the modelling. In the second part, the direct torque control (DTC) is elaborated; the control of the speed loop is ensured by an IP controller, the flux and the torque are estimated from source voltages and measured currents. The last part of this work is devoted to the operating system without mechanical sensor, using a Kalman filter as a speed observer. Simulation results are presented to verify the effectiveness of the proposed approach.

This paper presents a novel speed estimation algorithm for sensorless speed control in a direct stator flux orientation of an induction motor. The speed observation uses the stator currents and voltages as inputs and, contrary to what is proposed this last decade, is achieved in a reference frame linked to the stator current space vector. This leads to a simple equation for speed calculation which is a function of the stator flux. In direct stator flux oriented control (DSFOC), the stator flux is available for measurement or estimated. In the case presented in this paper, the stator flux is estimated using a reduced order Kalman Filter (KF) algorithm to reduce the computational requirements. The proposed speed estimation algorithm is tested numerically on an FPGA real-time simulation platform used as a tool for rapid control prototyping. Real-time simulation results show that accurate and fast speed estimation is achieved even at standstill.

This paper describes a new closed loop approach to estimate induction machine speed and rotor time constant from measured terminal voltages and currents for speed/torque sensorless control. For this purpose, a new state estimator, which eliminates flux information of the machine, is defined and a Lyapunov function is derived to determine speed and rotor resistance. The proposed algorithm is analyzed and verified experimentally.