A Comparative Study Between a Classical and Optimal Controller for a Quadrotor

Saraf, Prathamesh; Gupta, Manan; Parimi, Alivelu M.

doi:10.1109/indicon49873.2020.9342485

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…As mentioned before, the quadrotor in the presence of external disturbances. As mentioned before, the MUIR technique is a UIR based control law proposed in [9] and developed in a series of papers (See [20] and [13]), it was originally created for the output regulation of a class of nonlinear minimum phase systems in the case of asymptotically constant references. For the application of MUIR, the only necessary knowledge about the system to be controlled is its relative degree and the sign of the high frequency-gain 𝐿 𝑔 𝐿 𝑓 𝜌−1 ℎ, where 𝐿 denotes the Lie derivative of 𝑓(𝑥) and 𝑔(𝑥) nonlinear dynamic functions of the system and ℎ(𝑥) is the output to be controlled.…”

Section: Controller Designmentioning

confidence: 99%

“…In the literature it is possible to find linear and non-linear control laws as a solution to the regulation and tracking problems of this type of UAVs [6,7,8]. Linear control techniques have been implemented in several research projects and have proven to be able to regulate the quadrotor system, however, they are only valid while operating close to the operating state, like in [9] or around a predefined equilibrium condition, in which, a linearized dynamic model is built, like in [10]. It is worth to mention that the design of non-linear flight controllers offers a precise and robust control and represent a very important step in the design of fully autonomous vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Modified Universal Integral Regulator for Quadrotor UAV in the Presence of External Disturbances

Campos,

Díaz-Méndez,

Sousa

et al. 2024

ACRI

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Universal Integral Regulator (UIR) is a widely used control law for control problems, it is based on sliding mode control with the inclusion of a Conditional Integrator (CI). In the original approach of this control law, the main gain is considered a constant. The purpose of this paper is to solve a quadrotor tracking problem using a new nonlinear control law based on the quadrotor tracking problem using a new nonlinear control law based on the UIR, considering the main gain dependent on the tracking error (variable gain), the control law is called in the present work as Modified Universal Integral Regulator (MUIR). It is expected that the MUIR can improve the transient response and reduce the control demand when compared to previous approaches of similar controllers. The adopted Newton-Euler quadrotor model and the controller design are treated separately in two subsystems, attitude and position control loops. The stability of MUIR is demonstrated by the use of a candidate Lyapunov function. Finally, in order to validate the robustness and choice of the proposed controllers, several numerical simulations were developed in the presence of external disturbances. Less error and control activity during transient response were observed when compared to the original Universal Integral Regulator controller.

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Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modified Universal Integral Regulator for Quadrotor UAV in the Presence of External Disturbances

Campos,

Díaz-Méndez,

Sousa

et al. 2024

ACRI

View full text Add to dashboard Cite

show abstract

“…Authors in reference [21] did a comparative study between LQR and PID controller's performance on the quadrotor system. First, the authors drive a linear mathematical model of system dynamics by Newton-Euler's approach and then simulate the two designed control techniques under the same initial condition.…”

Section: A Related Workmentioning

confidence: 99%

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

Madebo,

Abdissa,

Lemma

et al. 2024

IEEE Access

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In this paper, an intelligent Model Reference Adaptive Control (MRAC) based on a neural network is proposed for robust tracking control of quadrotor UAV under external disturbances and parameter variations. First, the singularity-free dynamic model of the quadrotor is developed using Newton-Quaternion formalism. Then, conventional MRAC is designed to generate training data. With the generated data, the Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) are trained offline to get an initial set of network parameters for position controller parameters estimation and attitude control of the quadrotor, respectively, and an online learning algorithm is developed to update those network parameters in real-time. Finally, the performance of the designed Neural network-based MRAC has been evaluated using a numerical simulation in a nominal scenario and by introducing parametric variation and external disturbances as matched and unmatched uncertainties into the system. The simulation results show that the proposed controller has a better tracking performance and disturbance rejection capability compared with the Linear Quadratic Regulator (LQR) and conventional MRAC. Furthermore, the utilized control efforts are minimal and smooth proving functional safety and economical use of the controller. Therefore, the suggested controller is feasible for real-time implementation of the quadrotor UAV.

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“…The state-space equations for the attitude, position, and altitude control systems are expressed in Eqs. (6)(7)(8) as follows.…”

Section: Mathematical Model Of Quadcoptermentioning

confidence: 99%

Autonomous Quadcopter Control System Design Using LQG Controller to Perform Obstacle Avoidance

Darwito¹,

Alfathrah²,

Nugroho³

et al. 2022

Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Mechanical and E

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This research presents the implementation of the Linear Quadratic Gaussian (LQG) Controller and LiDAR sensors on the quadcopter used to avoid obstacles. The LQG controller is a combination of a Linear Quadratic Regulator (LQR) with a Kalman Filter as a State Estimator. The results of the estimate by Kalman Filter are compared against the actual value of the measurement by reviewing the Mean Square Error value. Linear position and angular position are variables that are controlled in the LQG control system to perform obstacle avoidance. The variations used in this study are the Q and R matrix values in the LQG controller design. Some of the tests that have been carried out include the open loop test, the closed loop test, the quadcopter test without an obstacle and the quadcopter test with an obstacle. The test results show that the quadcopter is able to follow the flight reference path and that the obstacle avoidance algorithm designed produces a quadcopter that is able to avoid obstacles. The test results show that the Q and R matrices on the Gain Regulator and Kalman filters can affect the quadcopter in carrying out avoidance.

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A Comparative Study Between a Classical and Optimal Controller for a Quadrotor

Cited by 13 publications

References 6 publications

Modified Universal Integral Regulator for Quadrotor UAV in the Presence of External Disturbances

Modified Universal Integral Regulator for Quadrotor UAV in the Presence of External Disturbances

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

Autonomous Quadcopter Control System Design Using LQG Controller to Perform Obstacle Avoidance

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