Model Predictive Control
Model Predictive Control is an optimization based control method. It offers several benefits, including fast dynamic responses, straightforward handling of input and output constraints and high scalability to multiple-input multiple-output plants. Advanced computing and more powerful hardwares are quickly bridging the gap between academic research and industrial applications in embedded systems.
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Sensorless control
The position sensor increases the cost and the size of electric drives and, more importantly, it reduces the reliability of the system.
High speed and low speed drives implement different technologies, namely back-electro motive force observers and high frequency signal injections, respectively. Accurate position estimation requires an in-depth knowledge of the motor, which can be achieved by self-commissioning procedures.
Parameter identification
Model based control techniques have been extensively investigated in the last years due to the outstanding achievable control perfromance. Then, the accurate knowledge of motor parameters is becoming of paramount importance. Many estimation algorithms have been proposed with the aim of identifying the engine under different operating conditions, e.g., during self-commissioning of the inverter, on a laboratory test bench or able to track any changes in parameters during the normal motor operation.
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