Parameter Optimization of Washout Algorithm Based on Improved Sparrow Search Algorithm

The success of the simulator's motion platform hinges on the washout algorithm; incorrect choice of its filter parameters can lead to issues like phase delay and reduced space consumption. Consequently, the study focuses on enhancing the sparrow search algorithm (Tent Golden Sparrow Search Algorithm (TGSSA)) to fine-tune the filter settings of the washout algorithm. Initially, Tent mapping is implemented to start the population, enhancing its diversity, hastening the algorithm's convergence, and boosting convergence precision; secondly, to address the issue where altering each sparrow discoverer and follower results in a halt in the local population's quest for the best solution towards the algorithm's end, Cauchy-Gaussian variance factors are incorporated during the follower position update, enhancing the algorithm's overall search efficiency. An optimization model is established to compare the filter parameters optimized by TGSSA with those optimized by other intelligent optimization algorithms. The results show that the new parameters obtained by the improved sparrow search algorithm enable the simulator motion platform to recover to the neutral position in a shorter time, improve the utilization of the motion platform, and reduce the phase delay.