Repetitive control mechanism of disturbance cancellation using a hybrid regression and genetic algorithm

• The analytic method of repetitive control proposed in this paper eliminates the interference from a feedback control system.
• The start point influences the convergence rate directly.
• When the data are stabilized, the tracking error of the obtained convergence value, 10−14, is the optimal solution.

The application of a repetitive control mechanism for use in a mechanical control system has been a topic of investigation. The fundamental purpose of repetitive control is to eliminate disturbances in a mechanical control system. This paper presents two different repetitive control laws using individual types of basis function feedback and their combinations. These laws adjust the command given to a feedback control system to eliminate tracking errors, generally resulting from periodic disturbance. Periodic errors can be reduced through linear basis functions using regression and a genetic algorithm. The results illustrate that repetitive control is most effective method for eliminating disturbances. When the data are stabilized, the tracking error of the obtained convergence value, 10−14, is the optimal solution, verifying that the proposed regression and genetic algorithm can satisfactorily reduce periodic errors.