ANFIS-SMC for Trajectory Tracking and Obstacle Avoidance Control of Quadcopter

In order to assess the effectiveness of the suggested ANFIS-SMC controller in trajectory tracking and obstacle avoidance control of a quadcopter, a comparative simulation analysis is presented in this research. ANFIS controller enables optimal output depending on a set of rules by establishing a variable gain rather than a fixed gain for the SMC controller. A Neuro-Fuzzy adaptive inference system is used to update the position control gain, enabling the sliding mode control to adjust to changing environmental conditions. To evaluate the effectiveness of the suggested technique, ANFIS-based SMC and SMC controllers (ANFIS-SMC) were compared and contrasted. ANFIS-SMC controller and SMC controller comparisons were made in four simulations. In the simulations, a quadcopter was moving along a square trajectory, both in the absence of obstacles and with varying distances between them. Its outcomes. The findings showed that the ANFIS-SMC controller performs better than the SMC controller in minimizing error. The mean absolute error generated by ANFIS-SMC is 0.064 m. Additionally, ANFIS-SMC outperforms the SMC controller by leading to safer obstacle avoidance, with the probability of collision being 0% while SMC is 33.33%. However, the ANFIS-SMC controller requires a longer execution time. The average execution time required by SMC is 45.2% faster than ANFIS-SMC.