Trajectory Tracking of a Tri-Rotor Aerial Vehicle Using an MRAC-Based Robust Hybrid Control Algorithm

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad; Masroor, Suhaib

doi:10.3390/aerospace4010003

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…A Fuzzy control was applied to set the PID-ILC parameters to restrain the influence of uncertain factors and improve the control precision [10]. A combination of Fuzzy control and PID control was applied to tune the control parameters of a model reference adaptive control for the altitude and attitude control of a tricopter [11]. A combination of the integral back-stepping control with the sliding mode control was studied for stabilization of a quadrotor attitude and to accomplish the task of trajectory tracking control [12,13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Trajectory Tracking Control of a Quadrotor Based on Iterative Learning Algorithm

Farzaneh¹,

Tavakolpour-Saleh²

2020

Journal of Engineering Technology and Applied Sciences

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This paper presents a new adaptive and optimal algorithm for the trajectory tracking control of a quadrotor using iterative learning algorithm (ILA) and enumerative learning method. Ordinarily the ILA, as an adaptive method, can perform well with PID control to improve the controller's performance for a nonlinear system. Quadrotors are considered as nonlinear and unstable systems in which the use of an adaptive and optimal controller can increase its stability and decrease error level. In this method, a PID controller is proposed for the inner and outer control loops of a quadrotor and the ILA is used to adapt PID control gains. Subsequently, an enumerative learning algorithm is used to optimize the learning rates of the ILA. For this purpose, at first, the dynamic model of the quadrotor is acquired. After that, the structure of the control system and the inner and outer control loops are defined. In the end, the simulation results for the trajectory tracking control are demonstrated. Through simulation, it is concluded that as time increases, the performance of the suggested control method in trajectory tracking control becomes better and better and error signals convergence to zero.

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Section: Introductionmentioning

confidence: 99%

Adaptive Trajectory Tracking Control of a Quadrotor Based on Iterative Learning Algorithm

Farzaneh¹,

Tavakolpour-Saleh²

2020

Journal of Engineering Technology and Applied Sciences

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“…Over the last decade, several robust approaches have been proposed for the control of unmanned aerial vehicles (UAVs), most of which use intelligent and robust control approaches such as fuzzy logic control (FLC), the H∞ technique, sliding mode control (SMC), the backstepping technique, and adaptive control [1][2][3][4][5][6][7][8]. The quadrotor is among the most popular UAVs and is widely used in many applications such as surveillance, air mapping, inspection, aerial cinematography, rescue missions, search missions in hostile environments, etc.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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“…The Tri-Rotor drone is a modern multi-rotor UAV type used in many applications such as target acquisition, reconnaissance, battle damage assessment, monitoring, imaging of forest fires, transportation, and many dangerous tasks [3]. There are some motivations that Tri-rotor is chosen over quad-rotor such as [4]: I. A clear alignment in the long distances of the tri-rotor contrast to the quad-rotor type, which is dissipated at long distances.…”

Section: Introductionmentioning

confidence: 99%

Tri-copter Drone Modeling with PID Control Tuned by PSO Algorithm

Nada¹,

Hadi²

2018

IJCA

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The tri-rotors1 are more recent kinds of drones, as compared with the mostly used quad-rotors1 because of the numerous special characteristics over the other types of multi-copters. Many technical features specialize tri-copters like small volume that is useful in slender places, light weight, extended battery existence, and agility in translation and turns. In this paper, (single tri-rotor) design is theorized and the nonlinear mathematical model is derived completely by Newton-Euler formula then the Proportional1-Integral1 and Derivative1 (PID) controller1 is utilized to control the rotational and translational equations1, six PID controllers are used for six Degrees of Freedom (DOF) equations of the model with the associated parameters are tuned by Particle1 Swarm1 Optimization1 (PSO) method to minimize the whole Integral1 Time1 Absolute1 Errors1 (ITAEs) for the tri-rotor model and the effects are gained by Simulink1 in MATLAB1. The results were satisfactory with the stability of the system and with little delay."

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Trajectory Tracking of a Tri-Rotor Aerial Vehicle Using an MRAC-Based Robust Hybrid Control Algorithm

Cited by 9 publications

References 33 publications

Adaptive Trajectory Tracking Control of a Quadrotor Based on Iterative Learning Algorithm

Adaptive Trajectory Tracking Control of a Quadrotor Based on Iterative Learning Algorithm

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

Tri-copter Drone Modeling with PID Control Tuned by PSO Algorithm

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