Differential Evolutionary Algorithm with Local Search for the Adaptive Periodic-Disturbance Observer Adjustment

Periodic disturbances occur during repetitive operations, and compensation for the periodic disturbances is an important issue to realize precise machine works because the periodic disturbances deteriorate the control precision. In addition, the periodic disturbance becomes a frequency-varying periodic disturbance when the periodicity of the operations changes, which complicates the compensation. To eliminate the frequency-varying periodic disturbances, an adaptive periodic-disturbance observer (APDOB) was proposed. However, the APDOB has a problem that the design of the APDOB is complicated with six design parameters. This paper proposes a differential evolutionary algorithm with local search that optimizes the six design parameters of the APDOB for the optimal frequency-varying periodic disturbance compensation. The proposed method based on a memetic algorithm framework can explore globally using the differential evolutionary algorithm and explore locally using the local search including the Levy flight. Moreover, the proposed method can reduce the number of the design parameters.