Modeling Method And Adaptive Control Of Brushless DC Motor Drive System Based On Event Sampling

In engineering practice,often due to economic cost or installation space,the brushless DC motor system cannot add additional speed sensors,and only rely on the Hall sensor inside the motor for data sampling.The brushless DC motor drag system uses the internal Hall sensor of the motor to detect the rotor position,and triggers sampling whenever the rotor reaches a certain position.In such an event sampling system,the sampling frequency will inevitably lead to sparse and non-sampling data Uniformity does not meet the needs of building accurate models.Aiming at this engineering practical problem,this paper presents a modeling method of brushless DC motor drag system based on event sampling data.This method uses the principle of gray box modeling.First,a non-linear Kalman filter is designed to perform noise reduction on the original data,and then the parameter model is identified online using the exponentially decaying least square method to establish a brushless DC motor.An exact model of the drag system.Then,considering that the variable load condition is common in the application process of the brushless DC motor drag system,a model reference adaptive controller for load disturbance is designed.After experimental verification,compared with the traditional incremental PID controller,the newly designed controller has greatly reduced the steady-state error and significantly improved the anti-load disturbance of the system in the case of variable load and sudden load.ability.This paper mainly studies the modeling of brushless DC motor drag system based on event-triggered sampling and its adaptive controller design issues,including model parameter identification,adaptive controller design,and controller performance analysis under event sampling data.The research contents and innovations of this article are as follows:First,academic problems are condensed from practical engineering problems.In engineering practice,the brushless DC motor drag system uses the internal Hall sensor of the motor to detect the rotor position.When the rotor reaches a certain position,sampling is triggered.In such an event sampling system,the sampling frequency will inevitably lead to sampling data.The sparseness and non-uniformity cannot meet the needs of building accurate models.Aiming at this practical problem,this paper condenses a new problem of establishing an accurate model of a brushless DC motor drag system based on event sampling data.Second,the problem of model building of the brushless DC motor drag system based on event sampling.This paper presents a method for modeling a brushless DC motor drag system based on event sampling data.This method uses the principle of gray box modeling.First,the parameter model of the brushless DC motor is established by using the white box modeling method,and then the parameter model is identified by the black box modeling method.In the parameter identification process,a non-linear Kalman filter was first designed to reduce noise on the original data,and anexponential attenuation least square method for non-uniform data was derived to identify the system's parameter model.Finally,numerical simulation and real experiments verify that the proposed method can effectively model the system.Compared with the traditional recursive least squares method,the method has better tracking performance of parameters in time-varying parameter systems.The model is more accurate.Third,from the perspective of the practical application of the brushless DC motor drag system,based on the existing models,a model reference adaptive controller for load disturbances is designed,and the controller is verified by numerical simulation and real experiments.Feasibility.Then,the model reference adaptive controller and incremental PID controller were applied to the brushless DC motor drag system,respectively,and the controller performance analysis was performed under the conditions of no load,constant load,sudden load and variable load.The experimental data show that the model reference adaptive controller and the incremental PID controller have comparable control effects under no load or constant load.When using model reference adaptive control,the system's steady-state error is smaller,and PID is used.The controller responds faster.In the experiments of system sudden load and variable load,when the model reference adaptive controller is used,the system's robust performance and anti-load disturbance ability are obviously higher than the incremental PID.small.It can be seen that if the system is often in a variable load condition,the performance obtained by using the model reference adaptive controller is better.In engineering practice,the system often works under variable load conditions.Therefore,the controller can better expand the application scenarios of brushless DC motors,and has positive significance for promoting the application of brushless DC motors.