Robust Full Tracking Control Design of Disturbed Quadrotor UAVs with Unknown Dynamics

Abstract

In this study, we develop a rigorous tracking control approach for quadrotor unmanned aerial vehicles (UAVs) with unknown dynamics, unknown physical parameters, and subject to unknown and unpredictable disturbances. In order to better estimate the unknown functions, seven interval type-2-adaptive fuzzy systems (IT2-AFSs) and five adaptive systems are designed. Then, a new IT2 adaptive fuzzy reaching sliding mode system (IT2-AFRSMS) which generates an optimal smooth adaptive fuzzy reaching sliding mode control law (AFRSMCL) using IT2-AFSs is introduced. The AFRSMCL is designed a way that ensures that its gains are efficiently estimated. Thus, the global proposed control law can effectively achieve the predetermined performances of the tracking control while simultaneously avoiding the chattering phenomenon, despite the approximation errors and all disturbances acting on the quadrotor dynamics. The adaptation laws are designed by utilizing the stability analysis of Lyapunov. A simulation example is used to validate the robustness and effectiveness of the proposed method of control. The obtained results confirm the results of the mathematical analysis in guaranteeing the tracking convergence and stability of the closed loop dynamics despite the unknown dynamics, unknown disturbances, and unknown physical parameters of the controlled system.

Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking

“…Physically, these virtual controls mean that the translation motion along x, y and z are controlled indirectly by the three common inputs (U 1 , φ, and θ). These virtual controls are given by (34). The desired trajectories of roll (φ d ) and pitch (θ d ) are obtained by inversing (34).…”

“…These virtual controls are given by (34). The desired trajectories of roll (φ d ) and pitch (θ d ) are obtained by inversing (34). .…”

“…Let e = [e 1 e 2 e 3 e 4 e 5 e 6 ] T = X d − X be the tracking error. Then, the sliding surface can be defined as in [34] and [35].…”

New Trajectory Tracking Approach for a Quadcopter Using Genetic Algorithm and Reference Model Methods

“…Likewise, in [11], a fuzzy logic algorithm was used to determine the optimal controller coefficients of a high order sliding mode controller for a quadcopter trajectory tracking control in the presence of uncertainties. More recently, a robust full tracking control design of disturbed quadrotor UAVs with unknown dynamics based on type-2-adaptive fuzzy systems are presented in [12]. Particularly, fuzzy logic has been used in a landing path selection problem for an aircraft carrier in [13]; the fuzzy path selection strategy was proposed, considering the fuzziness of environmental information and human judgment, with the goal of providing the pilot with a more reasonable set of choices.…”