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Sensorless Output Feedback Control for SPMSM and IPMSM

  • Alain GlumineauEmail author
  • Jesús de León Morales
Chapter
Part of the Advances in Industrial Control book series (AIC)

Abstract

In this chapter, the observers previously designed in Chap.  3 are associated with the control strategies proposed in Chap.  4, in order to achieve the SPMSM and the IPMSM sensorless controls. These observer-control schemes assume that only the currents and the voltages are available from measurements. Furthermore, these schemes are tested on the benchmarks specified in Chap. 1, Sect.  1.6. The first part of this chapter is devoted to the observer-control scheme where the control is designed using the backstepping techniques, and in the second part, the controller is designed using high order sliding mode techniques. For the backstepping control case, an observer-controller scheme applied to the SPMSM is constituted by a backstepping controller combined with an adaptive interconnected observer. Next, for the IPMSM, an adaptive interconnected observer combined with an integral backstepping controller is introduced. For both control-observer schemes, sufficient conditions are obtained to ensure the tracking stability of the closed-loop systems. For the high order sliding mode control case, first for the SPMSM, a sliding mode observer is associated with a quasi continuous high order sliding mode controller. Next, for the IPMSM, an observer-control scheme constituted of an adaptive observer combined with a finite time convergence high order sliding mode controller, by following a Maximum Torque Per Ampere (MTPA) strategy, is analyzed. Thanks to the benchmark trajectories previously introduced in Chap. 1, Sect.  1.6, significant simulation results illustrate the robustness and performance of all these observer-controller schemes.

Keywords

Stator Resistance Load Torque Nominal Case Practical Stability Speed Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.IRCCyN/Ecole Centrale de NantesThe University of Nantes Angers Le MansNantesFrance
  2. 2.Facultad de Ingeniería Mecánica y EléctricaUniversidad Autónoma de Nuevo LeónNuevo LeónMexico

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