This paper conducts a thorough analysis of the current literature on the effects of multiple controllers on quadrotors, focusing on two separate approaches: (i) controller hybridization and (ii) controller development. However, due to several drawbacks, such as high computation, a large amount of training data, approximation error, and the existence of uncertainty, the commercialization of those control technologies in various industrial applications is currently limited. Several control strategies are used, including Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), Backstepping, Feedback Linearization Control (FLC), Sliding Mode Control (SMC), and Model Predictive Control (MPC), Neural Network, H-infinity, Fuzzy Logic, and Adaptive Control. The quadrotor is an ideal platform for testing control strategies because of its non-linearity and under-actuated configuration, allowing researchers to evaluate and verify control strategies. Hence, the developed method is suitable for managing livestock with the average size of an adult sheep. The implementation of the obstacle avoidance scheme shows that the distance threshold of 1 m is sufficient for the autonomous navigation of the quadrotor. At the same time, the attitude controller records a settling time of 0.43 s, an overshoot of 2.50%, and a zero steady-state error. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage.

Quadcopter simulink model download