Quadrotor Controller Design Techniques and Applications Review

Shauqee, Mohamad Norherman; Rajendran, Parvathy; Suhadis, Nurulasikin Mohd

doi:10.13111/2066-8201.2021.13.3.15

Cited by 7 publications

(6 citation statements)

References 53 publications

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“…Hence, the metaheuristic, specifically the Fuzzy logic technique, is integrated into the PID controller to ensure the output is obtained as desired and the parameters are tuned automatically when changes are applied to the system. Fuzzy logic is a robust command that does not require precise knowledge of a mathematical model [7] [33]. By exploiting a human understanding of the plant in the control design process and decision-making, Fuzzy control works based on Fuzzy set theory, linguistic variables, and Fuzzy inference [34].…”

Section: Fuzzy Controllermentioning

confidence: 99%

“…The linear controllers, namely proportional-integral-derivative (PID), linear-quadratic-regulator (LQR), and H∞ have been widely used and effectively implemented in quadcopters. However, these methods possess limitations, with improperly tuned PID prone to instability, the less robustness of LQR leading to an inability to deal with nonlinearities, and the highly complex H∞ requiring crucial parameter adjustment [7]. Nonetheless, these methods have been commonly used from the early development of quadcopters until today due to their effectiveness and competence in obtaining stable flight conditions [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods possess limitations, with improperly tuned PID prone to instability, the less robustness of LQR leading to an inability to deal with nonlinearities, and the highly complex H∞ requiring crucial parameter adjustment [7]. Nonetheless, these methods have been commonly used from the early development of quadcopters until today due to their effectiveness and competence in obtaining stable flight conditions [7]. Hence, this work proposes a comprehensive approach by integrating fuzzy logic adaptability into the PID controller structure, yielding a self-tuning PID controller that improves stability and control precision.…”

Section: Introductionmentioning

confidence: 99%

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Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Baharuddin,

Basri

2023

IJRCS

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Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

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Section: Fuzzy Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Baharuddin,

Basri

2023

IJRCS

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“…The MPC controllers implemented in a drone are discussed in [21] (fixed-wing drone, though), [22] (simulation only), and [23] (authors struggled with MPC implementation on resource-limited hardware which resulted in a degraded MPC performance compared to PID). A great, comprehensive review of many controllers applied to multicopters can be found in [24]. However, most of the reviewed papers mentioned their focus on applying more advanced controllers, such as an MPC, to the higher control level only (i.e., the position control), leaving the low-level attitude controller 'as is' (usually just a PID or PD).…”

Section: Related Workmentioning

confidence: 99%

How Much Energy Do We Need to Fly with Greater Agility? Energy Consumption and Performance of an Attitude Stabilization Controller in a Quadcopter Drone: A Modified MPC vs. PID

Okulski

Ławryńczuk

2022

Energies

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Increasing demand for faster and more agile Unmanned Aerial Vehicles (UAVs, drones) is observed in many scenarios, including but not limited to medical supply or Search-and-Rescue (SAR) missions. Exceptional maneuverability is critical for robust obstacle avoidance during autonomous flights. A novel modification to the Model Predictive Controller (MPC) is proposed, which drastically improves the speed of the attitude controller of our quadcopter drone. The modified MPC is suitable for the onboard microcontroller and the 400 Hz main control loop. The peak and total energy consumption and the performance of the attitude controllers are assessed: the modified MPC and the default Proportional-Integral-Derivative (PID). The tests were conducted in a custom-implemented Flight Mode in the ArduCopter software stack, securing the drone in a test harness, which guarantees the experiments are repetitive. The ultimate MPC greatly increases maneuverability of the drone and may inspire more research related to faster obstacle avoidance and new types of hybrid attitude controllers to balance the agility and the power consumption.

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“…Literature reviews such as [14][15][16][17] present a thorough evaluation of the essential state-of-the-art control techniques which could be efficiently employed to quadcopters, such as Backstepping control (BC), MPC, Sliding Mode Control (SMC), Linear-Quadratic Regulator (LQR), H-infinity, Proportional-Integral-Derivative (PID), Adaptive control, Fuzzy logic and Neural Network control, Feedback Linearization (FL) control. Since the quadcopter is a nonlinear system, a few nonlinear control methods have obtained good results in trajectory tracking difficulties such as sliding mode control, nonlinear model predictive control (NMPC), backstepping control design and state feedback linearization control as seen in [12,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

An optimal hybrid quadcopter control technique with MPC-based backstepping

Nwafor,

Eneh,

Ndefo

et al. 2024

Archives of Control Sciences

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Quadcopter unmanned aerial vehicle is a multivariable, coupled, unstable, and underactuated system with inherent nonlinearity. It is gaining popularity in various applications and has been the subject of numerous research studies. However, modelling and controlling a quadcopter to follow a trajectory is a challenging issue for which there is no unique solution. This study proposes an optimal hybrid quadcopter control with MPC-based backstepping control for following a reference trajectory. The outer-loop controller (backstepping controller) regulates the quadcopter’s position, whereas the inner-loop controller (Model Predictive Control) regulates its attitude. The translational and rotational dynamics of the quadcopter are analyzed utilizing the Newton-Euler method. After that, the backstepping controller (BC) is created, which is a recurrent control method according to Lyapunov’s theory that utilizes a genetic algorithm (GA) to choose the controller parameters automatically. In order to apply a linear control technique in the presence of nonlinearities in the quadcopter dynamics, Linear Parameter Varying (LPV) Model Predictive Control (MPC) structure is developed. Simulation validated the dynamic performance of the proposed optimal hybrid MPC-based backstepping controller of the quadcopter in following a given reference trajectory. The simulations demonstrate the fact that using a command control input in trajectory tracking, the proposed control algorithm offers suitable tracking over the assigned position references with maximum appropriate tracking errors of 0.1 m for the �� and �� positions and 0.15 m for the �� position.

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Quadrotor Controller Design Techniques and Applications Review

Cited by 7 publications

References 53 publications

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

How Much Energy Do We Need to Fly with Greater Agility? Energy Consumption and Performance of an Attitude Stabilization Controller in a Quadcopter Drone: A Modified MPC vs. PID

An optimal hybrid quadcopter control technique with MPC-based backstepping

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