A self-adjusting multi-objective control approach for quadrotors

Abstract

The quadrotor represents a class of highly nonlinear dynamic systems and its controllability features are challenging. Hence, it serves as an ideal unmanned aerial vehicle platform to validate many artificial intelligence-based research investigations. Nonetheless, most of the offline tuning approaches devote efforts to find near optimal control gains to regulate individually the decoupled motion directions. This work adopts a multi-objective self-adjusting search mechanism to actuate the motions of a quadrotor via deciding the control gains of the interacting loops simultaneously. This algorithm employs a first order low pass filter transfer function as an accepting approach for the tunning mechanism. The proposed approach is compared with a Genetic Algorithm and another nonlinear Proportional-Integral-Derivative approach to highlight the usefulness of the proposed mechanism. It was founded that quadrotor follows the desired trajectory with a small tracking error of less than 2% in the X-Y plane and less than 1 % error tracking error in the altitude Z. Also, it is recorded that MONLTA can overcome the simulated wind disturbances of 0.1 N.m as a disturbance torque.