Experimental Flight Testing a Quadcopter Autopilot Based on Predictive Cost Adaptive Control

Autopilots for quadcopters are typically designed with an inner-outer loop architecture consisting of cascaded P and PID controllers. While straightforward, this design requires tuning numerous gains through simulation and flight tests, and may not be sufficiently robust to disturbances. To alleviate tuning requirements and to provide greater robustness, the present paper proposes an autopilot based on predictive cost adaptive control (PCAC). As an indirect adaptive control extension of model predictive control (MPC), PCAC uses recursive least squares (RLS) with variable-rate forgetting (VRF) for online system identification. The present paper compares PCAC with fixed-gain PID control in both simulation and flight tests with and without disturbances, and the performance improvement is examined.