Parameter Identification And Feedforward Control Of Permanent Magnet Synchronous Linear Motor

Permanent magnet synchronous linear motor is a kind of direct conversion of electric energy into linear motor.Its structure is simpler than rotary motor,there is no intermediate transmission link,and still has better performance at low speed.Therefore,it is widely used in CNC machine tools,lithography machines and other fields.These application scenarios have higher requirements on speed and position accuracy.To get high-precision control effect,we must first get the mathematical model of the controlled object,and then design the controller according to the obtained model.In this paper,we will study the parameter identification of linear servo system and the design of the basis function feedforward controller.The parameter identification of the linear servo system is first studied from the batch least square algorithm.After the actual identification of the motion control experimental platform,it is found that the existing batch least squares algorithm and the recursive least square algorithm are not accurate.Because of the noise interference,the identification result and the power spectrum analysis can not be fitted in the oscillation.In order to solve the problem of inaccurate recognition caused by noise,a filter-based iterative learning least squares algorithm is proposed.The recognition algorithm introduces the filter into the least-squares algorithm,and uses iterative method to solve the nonlinear problem.The simulation and experimental results show that this algorithm can effectively identify the model parameters of linear servo system,especially for the the oscillation in servo system,the effect is more obvious.The PID control strategy is generally used in the traditional control of motor.This control method is simple and effective,and and has been widely used in many occasions in practical control.However,this method is too dependent on experience and requires rich experience of debugger.PID control strategy is controlled by feedback error,which leads to control lag.In order to make up the shortage of single PID and achieve higher control precision,the feedforward and feedback two degree of freedom control strategy is adopted in the experiment.The feedforward controller is designed with an iterative learning control algorithm based on a polynomial of basis functions,which describes the model of the controlled object in terms of basis functions.For solving the problem of optimal value in the algorithm,the optimal feedforward signal is obtained by iteratively changing the continuous transfer function to approximate the mathematical model of the control object.This method can effectively reduce the linear servo system in the course of tracking error.The weighted matrix in the algorithm makes the algorithm robust and the convergence speed can be adjusted by parameters,which makes the algorithm more flexible and has a wider range of applications.