Session
Poster Session 2
Location
Salt Palace Convention Center, Salt Lake City, UT
Abstract
This work presents the application of an automated PI controller tuning method based on Particle Swarm Optimization (PSO) for Control Moment Gyroscope (CMG) systems. CMGs are critical components in aerospace systems, offering high-torque amplification and precise attitude control capabilities. Their superior performance compared to Reaction Wheels (RWs) makes them ideal for demanding applications such as spacecraft orientation and agile maneuvering. At the core of CMG operation are Permanent Magnet Synchronous Motors (PMSMs), which are typically controlled using Field-Oriented Control (FOC) for accurate response. FOC relies heavily on the performance of PI controllers within the current and speed control loops. Achieving optimal performance with conventional PI tuning methods such as Ziegler-Nichols, manual tweaking, or heuristic approaches has become increasingly challenging. Factors such as sensor noise, system nonlinearities, and hardware inconsistencies hinder their effectiveness and limit generalizability. These challenges often lead to suboptimal performance and production delays, as manual fine tuning remains a time-consuming process when high system efficiency is required.
Document Type
Event
Application of Particle Swarm Optimization for Tuning PI Controllers in CMG-Driven PMSM Systems
Salt Palace Convention Center, Salt Lake City, UT
This work presents the application of an automated PI controller tuning method based on Particle Swarm Optimization (PSO) for Control Moment Gyroscope (CMG) systems. CMGs are critical components in aerospace systems, offering high-torque amplification and precise attitude control capabilities. Their superior performance compared to Reaction Wheels (RWs) makes them ideal for demanding applications such as spacecraft orientation and agile maneuvering. At the core of CMG operation are Permanent Magnet Synchronous Motors (PMSMs), which are typically controlled using Field-Oriented Control (FOC) for accurate response. FOC relies heavily on the performance of PI controllers within the current and speed control loops. Achieving optimal performance with conventional PI tuning methods such as Ziegler-Nichols, manual tweaking, or heuristic approaches has become increasingly challenging. Factors such as sensor noise, system nonlinearities, and hardware inconsistencies hinder their effectiveness and limit generalizability. These challenges often lead to suboptimal performance and production delays, as manual fine tuning remains a time-consuming process when high system efficiency is required.
