Study On Iterative Learning Control For Harnessing The Nonrepetitive Disturbance

Iterative Learning Control is a newly developed intelligent control method in the last two decades. The basic idea of this method is to use the previous experience and the output error to correct the current control signal and make the system output to track the desired trajectory accurately. The paper will study the non-repetitive disturbance in the process of the iteration learning control, propose a new iterative learning control algorithm to suppress interference of non-repetitive, and simulate to test the effectiveness of the new learning law, enhance the usability in the actual experimental device.Firstly, the paper introduced the basic principle of ILC, deeply analyzed the development process and research status of ILC. Secondly, the paper summed up the iterative learning control law and analyzed the simulation results to summarize the influence the disturbance affect the control system. Thirdly, in the basic of weighted PD-type iterative learning control law, and proposed a weighted PD-type exponential variable gain iterative learning control algorithm. The simulation results proved that when the number of iterations tends to infinity, the tracking error converge to zero. Last, the paper combined the observer and iterative learning control, analyzed the structural performance of the combination. Through the simulation results, the paper got that if combining the disturbance observer and iterative learning control, the new system can eliminate the benchmark error of the non-repetitive disturbances.In summary, the paper proposed two new iterative learning control methods, the two methods can largely inhibited the interference of non-repetitive disturbances. The paper didn't study the delay problems and the gain selection with the unknown system parameters for the iterative learning control of practical applications and the further study of the convergence condition.