This paper proposes a control strategy for robust estimation of the transformation angle error despite variations in motor parameters. The method focuses on identifying and compensating angle errors in Indirect Rotor Flux-Oriented Control (IRFOC). The main objective is to correct the misalignment between the (d, q) reference frame and the actual magnetic flux. In contrast to conventional methods that depend on adaptive techniques and real-time parameter estimation, the proposed strategy avoids the need of an online identification of motor parameters. To improve the accuracy of the transformation angle error estimation, the approach operates on multiple reference frames, including (α, β) frame, (d, q) frame, and a deviated rotating frame referred as the (d′, q′). It relies on a robust Sliding-Mode observer based on the Super-Twisting algorithm. Simulation results using the IRFOC scheme are provided to validate the performance of the proposed method.

This work proposes a unified back Active Electromotive Force (AEMF) observer for AC machines based on the SuperTwisting Algorithm (STA). This study aims to create a universal observer structure that could drive Synchronous Machines (SMs) and Induction Machines (IMs) without modifying the observer structure. It is shown that the proposed unified observer can be applied to SMs and IMs with minimum machine parameters, negating the need for machine-type knowledge, or at the very least, the rotor flux model. The active EMF, a generalized version of the original back EMF, is used to achieve this. The Lyapunov theory guarantees the convergence of the proposed observer. Finally, to confirm our theoretical results, the proposed observer was validated on a Permanent Magnet SM (PMSM) and IM using MATLAB/Simulink simulations and experimental tests conducted on a real test bench.

In this paper, the problem of electric vehicle (EV) charging at the workplace is addressed via a two-layer predictive algorithm. We consider a time of use (TOU) pricing model for energy drawn from the grid and try to minimize the charging cost incurred by the EV charging station (EVCS) operator via an economic layer based on dynamic programming (DP) approach. An adaptive prediction algorithm based on a non-parametric stochastic model computes the projected EV load demand over the day which helps in the selection of optimal loading policy for the EVs in the economic layer. The second layer is a scheduling algorithm designed to share the allocated power limit (obtained from economic layer) among the charging EVs during each charge cycle. The modeling and validation is performed using ACN data-set from Caltech. Comparison of the proposed scheme with a conventional DP algorithm illustrates its effectiveness in terms of supplying the requested energy despite lacking user input for departure time.

Many power electronic converters can be modeled using the theoretical framework of switched affine systems, which considers the instantaneous dynamics of the converter instead of its averaged model. In this class of systems, a certain number of subsystems is present and a control law is designed to orchestrate the switching among them. When the switching law is state-dependent, the closed-loop system is stabilized at the desired equilibrium through sliding mode dynamics, leading to infinite-rate switching. In this paper, a control strategy is proposed using constant-width hysteresis as a way of bounding the switching frequency at a finite value when two subsystems are present. In addition, an upper bound on the hysteresis width is provided so that the system is not stabilized at the equilibrium of a subsystem. Finally, simulation results illustrating the application of the proposed method to power electronic converters are presented. The relation between the switching law parameters and the current ripple is discussed for a dc-dc Boost converter and a Buck converter.