Design of LQR Controller for 3D Trajectory Tracking of Octocopter Unmanned Aerial Vehicle

Velagić, Jasmin; Osmić, Nedim; Klovo, Vedin; Lacevic, Halil

doi:10.1109/codit55151.2022.9803884

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…World field scholars have designed various controllers for UAVs, classical methods including Proportional Integral Derivative (PID), Linear Quadratic Regulator (LQR), Backstepping controller, and Sliding Mode Control (SMC) have been widely used in quadcopters, but each type has its pros and cons, for instance, PID controller can stabilize the position of quadcopters and successfully eliminate errors in steady state [23], but in cases with disturbance, its lengthy adjustment time, big overshoot, and large steady-state error are non-negligible disadvantages [24]. Compared with PID, LQR owns a better robustness in controlling UAV's position and orientation coordinates but its response is slower [25]. Backstepping controller has excellent tracking performance, rapid adjustment, good adaptability in handling underactuation problem with rigid negative feedback form [26], but its performance is inferior to SMC as it has difficulties in estimating variations in uncertainties and external disturbances [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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Objective of this study is to develop a novel, effective, and robust Sliding Mode Control (SMC) method for quadcopters (also called quadrotors) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for the purposes of enhancing trajectory tracking performance and realizing safe and reliable flight. In the paper, the ANFIS was combined with SMC technology to propose a scheme of adaptive robust controller, which is composed of three sub-controllers, x position controller, y position controller, and z position (altitude) controller. The proposed method can realize position tracking control of quadcopters in the presence of external disturbances. With the help of ANFIS, an adjustable gain rather than a fixed gain was established for the SMC controller, the optimal output could be attained based on a set of rules, and the position control gain was updated by ANFIS, enabling the SMC to adapt to environmental changes. Through modelling, simulation and comparison, experimental data verified that the proposed ANFIS-SMC controller outperformed conventional SMC controller in terms of convergence speed, robustness, accuracy, and stability with a maximum mean error of 0.125 meters in trajectory tracking. Research findings of this paper could contribute to the development of robust and responsive control strategies for Unmanned aerial vehicles (UAVs) trajectory tracking by providing valuable insights into the design of more effective and efficient control systems for UAVs, particularly in the context of dynamic environmental conditions.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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“…One of these aspects is positioning the UAV or tracking the desired trajectory under high uncertainty. Considering the trajectory tracking part, linear and nonlinear controllers have been applied to UAVs [7], [11], [16], [33], [34]. However, observing or detecting the UAV's position is another challenging problem in a real-time system because of the uncertain nature of its components, such as sensor quality and communication latency [22], [23].…”

Section: Introductionmentioning

confidence: 99%

An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

Candan,

Beke,

Mihaylova

2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a novel approach called the Interacting Multiple Model (IMM)-based Maximum Correntropy Student's T Filter (MCStF), which addresses the challenges posed by non-Gaussian measurement noises. The MCStF demonstrates superior performance compared to the IMM algorithm based on Kalman Filters (KFs) in both simulation environments and real-time systems. The Crazyflie 2.0 nano Unmanned Air Vehicle (UAV) model is used in the simulation validation, and results from 3000 independent Monte Carlo runs are shown. After getting the simulation results under monotonously changed non-Gaussian distribution, their performance results have been compared to each other. The same scenario has been applied in the real-time system using Crazyflie 2.0. Next, results from real-time tests are presented in which the position of Crazyflie 2.0 is estimated online.

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Design and Simulation of PID-Based Control System for UAV Quadcopters

Van Nguyen,

Nguyen

2024

Lecture Notes in Networks and Systems

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Design of LQR Controller for 3D Trajectory Tracking of Octocopter Unmanned Aerial Vehicle

Cited by 8 publications

References 19 publications

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

Design and Simulation of PID-Based Control System for UAV Quadcopters

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