Active Disturbance Rejection Based Iterative Learning Control For Non-Repetitive Disturbances

Iterative learning control(ILC)can accurately control complex repetitive plants,so it has attracted attention and research.In practical applications,iteration varying disturbance reduce the control effect of ILC.How to deal with non-repetitive disturbances has become one of the key points.It is also an important method to ensure the accuracy and stability of control in practice,and it has practical significance that can't be ignored.In this paper,two new observer-based active disturbance rejection ILC methods are proposed for linear continuous and discrete systems with non-repetitive disturbances.The main work of this paper is as follows:(1)For the molecular weight distribution(MWD)of polymer in chemical process,an active disturbance rejection control(ADRC)method based on MWD moments is designed.The simulation results show that ADRC has strong anti-disturbance ability in process control.The idea of introducing ADRC into iterative learning to solve non-repetitive disturbances is provided.(2)For the continuous system with disturbances,an iterative learning observer(ILO)is designed to estimate the total disturbances during the control process of the system.Then the total disturbances are decomposed into input and non-input channel disturbances.Then a parameter adaptive ILC law with disturbance compensation is designed.Convergence of the method is proved,and the design conditions of controller parameters are given.The simulation results show that the method has strong robust.(3)Based on the principle of extended observer,a disturbance observer is designed to estimate disturbances in discrete repetitive systems.Then an ILC law is designed to compensate the disturbance.The method considers both the time axis and the iteration axis of the system,and ensure that the observation and output error converges along the time and iteration axis,respective.Finally,the design method of the initial batch control law is given to improve the convergence speed of the output error.Compared with other methods,the simulation results show that this method has a strong ability to deal with disturbances,and verify the advantages of the initial batch control law design.In addition,this method has good control effect for part-unknown model.