Robust sensorless vector control of an induction machine using Multiobjective Adaptive Fuzzy Luenberger Observer

Digital Twin Model of Electric Drives Empowered by EKF

Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is the integration of cyber-physical systems into machinery to enable connectivity. The digital twin is a promising solution for addressing the challenges of digitally implementing models and smart manufacturing, as it has been successfully applied for many different infrastructures. Using a digital twin for future electric drive applications can help analyze the interaction and effects between the fast-switching inverter and the electric machine, as well as the system's overall behavior. In this respect, this paper proposes using an Extended Kalman Filter (EKF) digital twin model to accurately estimate the states of a speed sensorless rotor field-oriented controlled induction motor (IM) drive. The accuracy of the state estimation using the EKF depends heavily on the input voltages, which are typically supplied by the inverter. In contrast to previous research that used a low-precision ideal inverter model, this study employs a high-performance EKF observer based on a practical model of the inverter that takes into account the dead-time effects and voltage drops of switching devices. To demonstrate the effectiveness of the EKF digital twinning on the IM drive system, simulations were run using the MATLAB/Simulink software (R2022a), and results are compared with a set of actual data coming from a 4 kW three-phase IM as a physical entity.

Adaptive Cruise Control in Electric Vehicles with Field-Oriented Control

An adaptive cruise control system is highly used in conventional internal combustion engine vehicles, but due to the electrification of the vehicles, the adaptive cruise control system must be placed in relation with the electric motor control block. This paper presents the relationship between an adaptive cruise control system and the motor control in the case of an electric vehicle. An indirect filed-oriented control of an induction machine is considered for the electric traction system. The adaptive cruise control block computes the required acceleration to ensure the velocity set by the driver based on the set velocity, the actual velocity of the vehicle, a safe distance, and the relative distance between the host car and a leading car. The proposed system was implemented in MATLAB/Simulink and, also, in a low-scale laboratory experimental setup. The correct operation of the system was tested using a speed profile, and both systems, simulated and experimental, have the expected response. The velocity, acceleration, rotor speed, torque response, and relative and safe distance are the responses presented to prove the efficiency of the proposed implementation.

Optimization of Fuzzy Controller for Predictive Current Control of Induction Machine

An optimization procedure for type 1 Takagi–Sugeno Fuzzy Logic Controller (FLC) parameter tuning is shown in this paper. Ant colony optimization is used to obtain the optimal controller parameters, and only a small amount of post-optimization parameter adjustment is needed. The choice of controller parameters is explained, along with the methodology behind the criterion for objective function value calculation. The optimized controller is implemented as an outer-loop speed controller for Predictive Current Control (PCC) of an induction machine. The performance of the proposed control method is compared with that of several other model predictive control methods. The results show a 55% decrease in speed tracking error and 74% decrease in torque overshoot.