Research On Optimization And Application Of Exponential Variable Ga In Iterative Learning Con Trol

Iterative learning control is an important branch of intelligent control.Its control process is mainly to make the control output signal fully track the expected trajectory by constantly correcting the control input.Exponential variable gain Iterative learning control can accelerate the convergence speed of the system and improve the control performance of the system.Because its exponential factor is selected by experience and lacks theoretical guidance,it needs to be optimized.The main research content is as follows:Firstly,aiming at the problem that exponential variable gain Iterative learning control algorithm is difficult to improve and lacks optimization theory,a control gain optimization method of exponential variable gain Iterative learning control algorithm in linear time invariant single input single output system is proposed.By using Toeplitz matrix property and matrix iteration theory,the sufficient and necessary conditions for the convergence of single input single output discrete linear time-invariant system are derived,and the convergence of the method is proved.Secondly,the monotonic convergence condition of the method is derived using optimization theory,and the exact solution of the optimal control gain is obtained through the performance index function.Conclusion:When the exponential parameter is negative and the system is a single input single output linear discrete system,it has a faster convergence speed.This algorithm selects the optimal control strategy based on the system state equation,which can quickly and accurately calculate the optimal control value and improve the convergence speed of the system.Secondly,on the basis of theoretical analysis,the vector control model of permanent magnet synchronous motor based on exponential variable gain Iterative learning control is established by using SIMULINK simulation software,and several Iterative learning control algorithms are selected for comparative simulation experiments,and the simulation results are carefully analyzed and compared.The correctness of the optimal control theory of exponential variable gain Iterative learning control is verified by simulation.Finally,an experimental platform was built using hardware such as DSP28335 control chip,IPM motor driver board,and MiG permanent magnet synchronous motor.Compile the motor control program based on CCS software,write the exponential variable gain Iterative learning control algorithm,burn it into the DSP28335 control chip through the upper computer,conduct motor experiments based on different Iterative learning control algorithms,and observe the fluctuation of motor output value and peak value change,further verify the effectiveness of the exponential variable gain Iterative learning control optimal control algorithm,At the same time,it was verified that the control effect is better when the exponential factor is negative.In conclusion,the exponential variable gain Iterative learning control optimal control gain method proposed in this paper effectively improves the system output performance,reduces the system output error,improves the tracking effect on the expected trajectory,and further improves the convergence speed of the system.At the same time,the basis for selecting the exponential factor is given.Simulation and experimental results verify the effectiveness of the exponential variable gain Iterative learning control optimization method and related conclusions proposed in this paper.