What is it about?
This paper presents a new adaptive Backstepping technique to handle the induction motor (IM) rotor resistance tracking problem. The proposed solution leads to improve the robustness of the control system. Given the presence of static error when estimating the rotor resistance with classical methods, and the sensitivity to the load torque variation at low speed, a new Backstepping observer enhanced with an integral action of the tracking errors is presented, which can be established in two steps. The first one consists to estimate the rotor flux using a Backstepping observer. The second step, defines the adaptation mechanism of the rotor resistance based on the estimated rotor-flux. The asymptotic stability of the observer is proven by Lyapunov theory. To validate the proposed solution, a simulation and experimental benchmarking of a 3 kW induction motor are presented and analyzed.
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Why is it important?
The proposed solution was implemented in real-time for the indirect vector controlled three-phase induction motor drive. The adaptive indirect field-oriented control is based on a rotor-flux observer with an adaptation mechanism for the rotor resistance. The adaptive law which allows the estimation of the rotor resistance is determined using the Lyapunov stability theory. The simulation and experimental results showed that the MRAS rotor resistance observer doesn’t guarantee a good performance of the fact that the estimation error exceeds 10%. However, the proposed nonlinear Backstepping rotor resistance observer has good performances in terms of load disturbances and changing the speed reference, with an estimation error lower than 1%. Important advantages of the proposed algorithm include that is suitable for induction motor adaptive control, and guarantee a high-performance compared to other recent works.
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This page is a summary of: A novel adaptive control method for induction motor based on Backstepping approach using dSpace DS 1104 control board, Mechanical Systems and Signal Processing, February 2018, Elsevier,
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