Adaptive speed controllers based on artificial intelligent techniques for unmanned vehicle electric propulsion system

Brushless direct current motors (BLDC) are used in controlling the unmanned vehicles (UV) rotors dynamics. In this paper, speed control for BLDC motor via a set of adaptive intelligent control techniques is tackled. Adaptive neuro fuzzy inference systems (ANFIS), self-adaptive proportional - integral - derivative (SA-PID) and adaptive fuzzy sliding mode control (AFSMC) are applied to control the speed of the BLDC to maintain the required speed guaranteeing better performance, robustness and safety of the UV during mission. The main contribution in this paper lies in solving the speed control problem for a BLDC on board an UV via artificial intelligent control techniques ensuring the success of the UV in performing the required mission. The simulation results prove the success of designed controller to achieve the desired speed and the enhancement of the performance compared with traditional controllers.