Bi Input-extended Kalman filter based estimation technique for speed-sensorless control of induction motors

Sensorless Detection of Position and Speed in Brushless DC Motors using the Derivative of Terminal Phase Voltages Technique with a Simple and Versatile Motor Driver Implementation

“…This is determined with the number of rotor pole pairs according to Eq. (12). Considering the Maxon EC 45 motor, one mechanical cycle is equivalent to 8 electrical cycles and the resolution of detection method is 7.5º (mechanical degrees), which is demonstrated in Eqs.…”

“…Advanced sensorless techniques are estimation and model-based methods such as sliding-mode observer [11], Extended Kalman Filter [12], adaptative observers [13,14] and artificial neural networks [15]. The most common signals considered in position sensing and commutation techniques are the line to line motor voltages, motor terminal voltages in respect to the half of the DC bias, and terminal motor voltages regarding a virtual neutral point [2].…”

Bi input-extended Kalman filter-based speed-sensorless control of an induction machine~capable of working in the field-weakening region

“…Different from [22], the proposed BI-EKF-based observer in this study provides the online estimations of i sα , [17,[19][20][21] are combined in this study by utilizing the novel version of the BI-EKF technique in [22] in order to estimate all of the electrical variables of i sα , i sβ , φ rα , φ rβ ,R s , R r , and L m and the mechanical ones of ω m and t L , including the viscous term. L m estimation is required to extend the speed operation range of the IM motor drive to the field-weakening region.…”

“…However, both switching and braided EKF require 2 separate EKF algorithms; this is not desired from the real-time implementation point of view since the cost of hardware platforms such as microprocessors, digital signal processors, and field programmable gate arrays increase with the increasing memory size/area required for embedding the software algorithm. A significant step forward in this sense is the method in [20,21], where the introduced bi input-EKF (BI-EKF) technique merges the consecutively operated 2 separate EKF algorithms into a single EKF algorithm by successively switching its input/terms associated with the 2 IM models; thus, the BI-EKF technique considerably reduces the memory requirement of both switching and braided EKF. Moreover, a novel version of the BI-EKF technique was recently introduced in [22], which estimates the total inertia, j T , together with i sα , i sβ , φ rα , φ rβ , ω m , R s , R r , and t L by using the measured stator phase currents and voltages; therefore, it increases the number of estimated states and parameters compared to [20,21].…”

“…A significant step forward in this sense is the method in [20,21], where the introduced bi input-EKF (BI-EKF) technique merges the consecutively operated 2 separate EKF algorithms into a single EKF algorithm by successively switching its input/terms associated with the 2 IM models; thus, the BI-EKF technique considerably reduces the memory requirement of both switching and braided EKF. Moreover, a novel version of the BI-EKF technique was recently introduced in [22], which estimates the total inertia, j T , together with i sα , i sβ , φ rα , φ rβ , ω m , R s , R r , and t L by using the measured stator phase currents and voltages; therefore, it increases the number of estimated states and parameters compared to [20,21]. In other words, the novel version of the BI-EKF technique proposed in [22] estimates 5 common states ( i sα , i sβ , φ rα , φ rβ , and ω m ) plus 2 different parameters ( t L and R s or 1/j T and R r ) at each sampling time, T .…”