State of the Art in Reluctance Coilgun-Type Electromagnetic Launchers: A Review

Reluctance coilgun-type electromagnetic launchers are pulsed electromechanical systems that accelerate ferromagnetic projectiles by exploiting the tendency of magnetic circuits to minimize reluctance. Compared with railguns and induction coilguns, these launchers offer significant advantages such as structural simplicity, elimination of sliding electrical contacts, reduced mechanical wear, and ease of multistage modular implementation. These characteristics make them attractive for applications in laboratory-scale launch systems, industrial actuation, sports launchers, and educational platforms. Despite these benefits, practical deployment remains constrained by inherently low energy-conversion efficiency, switching losses, magnetic saturation, residual-current-induced reverse forces (suck-back), and high sensitivity to stage timing. Moreover, the strong coupling between electrical, magnetic, thermal, and mechanical subsystems further complicates performance optimization. This review provides a comprehensive overview of the operating principles, architectural configurations, and recent technological advancements in reluctance-coilgun-type electromagnetic launchers. Key focus areas include stage topology, discharge circuit design, timing and control strategies, armature optimization, energy-transfer efficiency, sensorless estimation techniques, and system-level scaling. Recent studies demonstrate that multistage systems can achieve projectile velocities of up to 130 m/s, driven by advanced discharge circuits, optimized switching strategies, and enhanced armature designs. However, when compared to railgun and advanced induction-launcher technologies, reluctance coilguns still face significant challenges in efficiency, repeatability, and scalability. Future research directions are expected to focus on integrated coil–pulse power co-design, residual energy recovery, model-based and AI-assisted control strategies, advanced magnetic materials, and application-specific system optimization.