High gain adaptive observer design for sensorless state and parameter estimation of induction motors

Abstract

This paper addresses the problem of accurately estimating the mechanical and magnetic state variables as well as the stator and rotor resistances of induction motors using only the stator current measurements and the supplied stator voltages from an appropriate nonlinear parametrization. The involved estimation is carried out by a high gain adaptive observer designed bearing in mind the available fundamental results together with the useful implementation features, namely conception simplicity and computational efficiency. An exponential convergence of the state and parameter estimation errors is established under admissible assumptions, namely the persistent excitation requirement has been particularly reduced thanks to the introduction of unknown parameter characteristic indices. The effectiveness of the adaptive observer is highlighted throughout simulation results involving a typical induction motor.

Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor

“…Moreover, this method is difficult to implement, because its gain comprises the sign function and other design parameters whose values must be updated when previously unknown singularities occur [2,11]. It is also sensitive to measurement noise [8,10]. Furthermore, it leads to lack of accuracy of speed estimation under unobservable conditions in the zero speed region as reported in [18].…”

“…Extended Kalman Filter (EKF) [27] is mainly motivated by its implementation simplicity and robustness to noise generated in the system. However, it suffers from the lack of guaranteed stability [6,8,9]. Moreover, the EKF technique is valid for a limited range around the operating point, which limits its dynamic performance.…”

“…Significant effort is spent in [8,10,20] to approximate the standard high gain observer with fixed gains for sensor-less speed estimation of an IM by exploiting a suitable procedure to tackle the singularities occurring whenever the observability is lost at zero and very low speeds. However, as shown in [8,10,20], the approximate observer still suffers from the so-called peaking phenomenon, highfrequency oscillation and large uncertainties especially in the unobservable regions. Indeed, as mentioned in [20,36,37,39], such drawbacks are due to the very large value of the observer gains which remain unchanged even in the presence of noise.…”

Discontinuous High-Gain Observer in a Robust Control UAV Quadrotor: Real-Time Application for Watershed Monitoring

“…Adaptive observers for linear systems with a parameter adaptation algorithm has been presented in [13,14]. Both state and disturbances are estimated for MIMO linear systems assuming that detectability and persistently exciting (PE) hold in [15][16][17][18]. As an alternative to the adaptive approach, various robust techniques have been considered.…”

Design of extended Kalman filter for SEPIC converter and comparison to Kalman filter

“…There are many robust nonlinear observers in the literature such as High Gain (HG) observer and Sliding Mode Observer (SMO). HG observers are designed by deriving transfer functions from nonlinear model uncertainty to estimation error and minimizing the H ∞ norm of this transfer function through the observer parameters [16]. Another nonlinear observer type, SMO uses switching functions to suppress the nonlinear terms and forces the error dynamics to be linear after a certain point [17].…”