Sliding Mode Observation Of Back-EMF For Brushless DC Motors Based On Online Parameter Combined Identification

Brushless DC motor (BLDCM) has attracted more and more attention in recentyears. Among all the position sensorless control strategies, the back-electromotiveforce (back-EMF) method is broadly studied and improved since it is simple andpractical, whose key step is to get the precise back-EMF of the motor. Sliding modestate observer (SMO) is the common strategy to obtain the back-EMF due to itsrobustness. However, since it is derived from the mathematical model of the motor,the variation of the resistance and inductance affect the observation.Taking such issues into account, the fundamental operating principle and themathematical model of BLDCM are stated in the dissertation, meanwhile, accordingto the requirements of the improvement and outstanding research achievements athome and abroad, a novel linear back-EMF SMO is designed, which can achievecorrect back-EMF observation for a BLDCM. Further, such a strategy is combinedwith the parameter identification theory to obtain a new adaptive SMO of theback-EMF observation for a BLDCM based on Lyapunov Stability Theory.The concrete procedure is as below. Considering the error of the stator resistanceand inductance, a SMO is designed. Afterwards, the condition that a system enters thesliding mode operation is involved to derive the parameter value range of the SMO,which ensures the value of the observed back-EMF can converge to its actual value indefinite time.In order to combine the SMO theory and the parameter identification theory, theeffect of the parameter error on the observation is analyzed; the relationship betweenthem is studied with such conclusion that the error of the inductance causesobservation error during current-varying moments; while that of resistance functionsin steady states based on the mathematical model of the motor. Subsequently,Lyapunov stability theory is referred to so that the parameter online combinedidentification law are obtained, whose identification results are fed back to theobserver to eliminate the effect it brings to the observation and the system. In thisdissertation, the experimental system is established based on the core processorTMS320F28335produced by TI company to testify the proposed theory. The resultsdemonstrate that the proposed adaptive SMO can not only observe the linearback-EMF accurately, but also identify the value of resistance and inductance of the motor reliably, which verifies the correctness and the rationality of the proposedstrategy.