Design of Control Strategies for Speed Regulation of Magnetic Reciprocating Engine

Magnetic reciprocating engines (MREs) offer an emission-free alternative to internal combustion engines; however, their practical implementation requires precise speed regulation, a challenge compounded by coupled electromechanical dynamics and inductive actuator lag. This paper presents the design, implementation, and experimental comparison of two intelligent control strategies for speed regulation in an MRE prototype: a neural ignition-angle controller that dynamically adjusts ignition timing using an online neural network, and a Voltage Feedforward Neural Network (FFNN) controller that combines a PID feedback loop with an offline-trained inverse neural model as a feedforward compensator. The experimental results confirm that both controllers achieve effective speed tracking. The FFNN approach demonstrates superior performance, exhibiting reduced control effort and a more stable transient response compared with the neural angle controller, while maintaining precise tracking across different operating conditions.