This paper addresses the problem of reducing the impact of periodic disturbances arising from the current sensor offset error on the speed control of a permanent-magnet synchronous motor. The new results are based on a cascade model predictive control scheme with an embedded disturbance model. Supporting experimental results, where the per-unit model is used to improve numerical conditioning, are also given.

Fixed-wing unmanned aerial vehicles (UAVs) have become increasingly important in military, civil, and scientific sectors. Because of the existing nonlinearities, effective control this type of UAV remains a challenge. This paper proposes a gain scheduled proportional-integral derivative (PID) control system for fixed-wing UAVs where a family of PID cascade control systems is designed for several operating conditions of airspeed. This is done using an automatic tuning algorithm, where the controllers are automatically selected by deploying an airspeed sensor positioned ahead of the aircraft. Furthermore, the actual gain scheduling is carried out by forming an interpolation between the family members of the linear closed-loop system, which ensures a smooth transition from one operating point to another. Experimental results are conducted in a wind tunnel to show the successful design and implementation of the gain scheduled control system for the fixed-wing UAV and the significant performance improvement over a linear control system without controller adaptation.