A Review on Comparative Remarks, Performance Evaluation and Improvement Strategies of Quadrotor Controllers

Abstract

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. 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. 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. 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. Besides, the limitations of the previous works are discussed, challenges and opportunities to work in this field are assessed, and potential research directions are suggested.