This study describes a sensorless speed control scheme for induction motors supplied by a multi-level inverter. The scheme exploits the low DC link voltage used in some of the multi-level converter configurations which employ H-Bridges. The rotor position is tracked by measuring the rate of change of motor stator currents when low-voltage test vectors are applied using the H-Bridges. In this way, the motor current distortion introduced by the sensorless control scheme is reduced compared to that seen when using a two-level converter. The proposed approach could therefore be applied to high-power motor drives, and automotive drive systems. The study presents a theoretical derivation of the algorithm and experimental results which show the improvement in the motor current quality achieved using the new technique compared to sensorless techniques implemented on a two-level inverter. Current distortion introduced using two-level convertersBoth the INFORM and FPE sensorless control methods track the rotor slotting saliency in an induction motor by deriving an estimate of the drive's incremental inductance. This is achieved by measuring the motor current derivatives (di/dt) in response to specific voltage vectors applied to the motor. The INFORM [4] method applies these test vectors as specific pulses during the zero or null vectors of the normal PWM waveform. As these test vectors introduce a voltage
Model-based adaptive controllers have been practiced with numerous successes. The controller is formed in a online discrete optimal controller and implemented in control computer. Because of the fast and accurate calculation capability of microcomputer, this type of controller has reached their limits. To explore the potentiality of model-based adaptive controller, we investigate the adaptive controller with an expert system for selection of identifiers. The model-based adaptive controller usually uses a recursive least squares identifier. This kind of identifier requires a lot of calculations. An alternative for adaptive function can be using an rule-based expert system to decide the need of updating process time series model. In addition, we can use a simplified recursive least squares identifier. This paper presents the formulation of this type of controller. Moreover, the simulations are carried out to test the practicality of such controller. The effect of such rule-based adaptation plus model-based optimization and controller formulation will be presented by accumulated loss versus sampling periods. The improvement of mean and standard deviation of controlled variable indicates the sophistication of combination of artificial intelligence and computation power of control computer.
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