Learning-based Thrust Regulation of Solid-Fuel Ramjet in Flight Conditions

This paper investigates the performance of a learning-based control system for regulating the thrust generated by a solid fuel ramjet engine in realistic flight scenarios. An integrated simulation framework is developed that combines a longitudinal missile dynamics model, a missile autopilot, a quasi-static engine dynamics model, and a learning controller for thrust regulation. The missile autopilot is based on the classical three-loop topology. The learning controller is an adaptive PID controller whose gains are recursively optimized using the retrospective cost adaptive control algorithm. First, harmonic acceleration commands are used to simulate variable flight conditions that affect the thrust generated by the engine model. Next, an interception scenario is simulated by integrating a guidance law in the loop. Numerical results indicate that the learning controller can regulate the generated thrust despite wide variations in operating conditions.