Intelligent Tuning of Microwave Cavity Filters Using Granular Multi-Swarm Particle Swarm Optimization

Abstract

The tuning of microwave cavity filters (MCFs) is a complex process to improve the filtering performance. In practice, the tuning is mostly conducted in a manual way, and thus is time and resource intensive. Toward the demand for automatic tuning of MCFs with high accuracy and efficiency, this article proposes an intelligent tuning method for MCFs via modeling and optimization. The main contributions are threefold as follows: 1) A series of performance evaluation functions are defined to comprehensively characterize the tuning output; 2) a block modeling method is proposed to construct the electromechanical characteristic model to facilitate the calculation of the cost function; 3) an improved particle swarm optimization (PSO) algorithm, named granular multi-swarm PSO (GMS-PSO), is proposed to achieve quick search of optimal combinations of the tunable components. The effectiveness and practicality of the proposed method are demonstrated by experiments with a real intelligent tuning system.