Nowadays, most brushless DC (BLDC) motors use Hall sensors or sensorless algorithms based on backelectromotive force (back-EMF) sensing to detect rotor position information. These methods detect the commutation moments but imply the use of rectangular stator currents which, according to recent literature, limits the energy efficiency. In this study, sinusoidal stator currents are used to increase the motor energy efficiency. As a consequence, standard control based on the feedback of the Hall sensors or based on sensorless techniques detecting the back-EMF zero-crossing cannot be used. Therefore, the authors propose a load angle control algorithm for BLDC motors without using position and speed sensors. The objective is to obtain energy-efficient sensorless control for the BLDC motor based on the measurement of only two current and one voltage signal. The energy saving potential of the proposed method is especially outspoken for fixed speed applications with varying loads, which are typical BLDC applications. Experimental results are presented to validate the proposed method. Energy efficiency measurements over the whole operating range of the BLDC motor are included and show an energy saving potential up to 9.5%.
The use of active car suspensions to maximize driver comfort has been of growing interest in the last decades. Various active car suspension control technologies have been developed. In this work, an optimal control for a full-car electromechanical active suspension is presented. Therefore, a scaled-down lab setup model of this full-car active suspension is established, capable of emulating a car driving over a road surface with a much simpler approach in comparison with a classical full-car setup. A kinematic analysis is performed to assure system behaviour which matches typical full-car dynamics. A state-space model is deducted, in order to accurately simulate the behaviour of a car driving over an actual road profile, in agreement with the ISO 8608 norm. The active suspension control makes use of a Multiple-Input-MultipleOutput (MIMO) state-feedback controller with proportional and integral actions. The optimal controller tuning parameters are determined using a Genetic Algorithm, with respect to actuator constraints and without the need of any further manual fine-tuning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.