A Fully Automated Robotic System for Tuning Optimization of RF Cavity Filter

ABSTRACT The advancements in radar systems, electronic warfare, and telecommunication industries have generated a substantial demand for microwave filters. Among these, cavity filters have conventionally served in transmitters and receivers, facilitating the passage of desired signals in the passband while effectively rejecting harmonics and spurious signals outside the desired frequency range. Each resonator, arranged perpendicular to the cavity filter block's length with precise spacing and alignment, is meticulously tuned to the band's center frequency and bandwidth. Post‐production tuning of radiofrequency (RF) filters is essential due to material and manufacturing tolerances. Traditionally, this tuning process has been performed manually. While necessary, manual tuning is time‐consuming and expensive, especially for high‐order filters. It further restricts precise adjustments, limits production scalability, and escalates manufacturing costs. To address these limitations, an advanced and automated approach is imperative. This study presents a robotic control architecture for cavity filter tuning, designed to leverage intelligent computer‐aided tuning processes. Specifically tailored for miniaturized tuning screw filters, the system operates fully autonomously, integrating collaborative robots (COBOTs), single and multi‐axis robotic arms, and a Cartesian platform. Additionally, it incorporates an image process system, force–torque sensors, and vector network analyzer (VNA) to monitor and measure relevant parameters during the tuning process. The RF tuning control algorithm, along with its subsections—the Control Algorithm of the Robotic System and the RF Tuning Algorithm—is thoroughly explained with a hierarchical main flow. All implementation processes, including the preparation for tuning and the tuning stages, are detailed. Image processing and search optimization algorithms are employed to determine all input and unknown parameters, while soft locking and thrust force vector optimization algorithms enhance tuning sensitivity. A sample cavity filter is tuned using the robotic system with real‐time monitoring on a VNA, utilizing both coarse and fine‐tuning algorithms. The RF performance, measurement results, and robotic iterations are presented, comparing the advantages and disadvantages of these tuning methods. The RF tuning methods and control algorithms adopt a data‐driven model, which will be further developed in future work.