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Prototyping and proper testing of the electrical control unit of electric vehicles under various load profiles and fault conditions is necessary to ensure maximum efficiency of the electric motors, batteries and power electronics. Hardware-in-the-loop simulation and power hardware-in-the-loop offer means to prototype and test various controllers while simulating key parts of the system. This paper...
An adaptive fuzzy logic control (FLC) scheme is presented for permanent magnet synchronous machines (PMSMs) as an alternative to conventional control techniques. Unlike other controllers, the proposed strategy achieves speed tracking without any current loop regulation, which yields reduced complexity. However, the absence of current control leads to higher energy consumption and limits the operation...
This paper presents a comprehensive modeling of a three-phase cage induction machine used as a self-excited squirrel-cage induction generator (SEIG), and discusses the regulation of the voltage and frequency of a self-excited SEIG based on the action of the static synchronous Compensator (STATCOM). The STATCOM with the proposed controller consists of a three-phase voltage-sourced inverter and a DC...
In recent years, it has been proven that one of the main issues in isolated applications is the importance of the dynamic characteristics assessment of three phase self-excited induction generator (SEIG). This paper presents a generalized state-space dynamic model using D-Q stationary reference frame of such a three phase SEIG. The values of self-excitation capacitance and the minimum and maximum...
In this work, a fuzzy logic control system is proposed for PMSMs without knowledge of the machine's parameters. The scheme consists of two adaptive fuzzy controllers, respectively for velocity and direct current control. The control scheme stability is proven by the Lyapunov stability theory and its performance is validated through a set of simulations on an experimentally validated PMSM model. The...
The well-known Brocket's theorem revealed that nonholonomic systems, hopping robots, for example, can not be stabilized by smooth time-invariant state feedback controllers. In this manuscript, we propose a linear time-varying state feedback controller for stabilizing a nonholonomic hopping robot during flight mode in finite time. The current approach is novel in the sense that we modify the Pontryagin's...
In this paper, an adaptive fuzzy control scheme is introduced for permanent magnet synchronous machines (PMSMs). The adaptive control strategy consists of a Lyapunov stability-based fuzzy speed controller that capitalizes on the machine's inverse model to achieve accurate tracking with unknown nonlinear system dynamics. As such, robustness to modeling and parametric uncertainties is achieved. Moreover,...
In this paper, we introduce an adaptive control methodology of piezoelectric actuators for microelectromechanical manipulation mechanisms. The adaptive control strategy capitalizes on the piezoelectric actuator's inverse model to design an adaptive controller using a Lyapunov-based adaptation technique. The controller achieves high precision motion tracking under hysteresis nonlinearities, parametric...
In this paper, a motion and balance control scheme is introduced for inverted pendulums using artificial neural network (ANN). The control strategy uses a trade-off strategy to achieve motion tracking and balance control simultaneously with a single controller. Unlike other neural control strategies, no offline learning or a priori system's dynamics knowledge is required. The controller is trained...
This paper introduces a robust artificial neural network (ANN) based nonlinear speed observer for permanent magnet synchronous machines (PMSMs). A multilayer perception is trained online using back-propagation learning algorithm to estimate the rotor speed without any a priori dynamics knowledge. Thus, the proposed observer is able to cope with higher degrees of nonlinearity since it is not based...
In this paper, we introduce an artificial neural network (ANN) based motion control methodology of micro actuators for microelectromechanical systems (MEMS). The control strategy is based on a multilayer perception (MLP) trained online using a Lyapunov-based learning technique. The controller achieves high precision tracking under unknown system dynamics including hysteresis and external disturbance...
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