A fuzzy adaptive controller design for integrated guidance and control of a nonlinear model helicopter

Pakro, Farhad; Nikkhah, Amir Ali

doi:10.1007/s40435-022-00993-7

“…This controller based on the state space representation used as integral feedback to make the system reach the steady state by minimizing the cost function quadratic [17]. There are also many other controllers such as Model Predictive Controller (MPC), neural-network, and fuzzy-logic, which are considered as learning-based controllers [18][19][20][21][22]. These controllers are advantageous as they can be used to adapt to changing conditions and can be used to optimize the performance of the quadcopter.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of PID, PD, LQR, and LQR-PD Regulators for Quadrotor Stabilization and Trajectory Tracking

Djizi

¹

,

Zahzouh

²

2023

Preprint

0

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Quadrotors, a type of unmanned aerial vehicle, utilize four rotors for precise lift and control, making them highly versatile for a variety of tasks, such as delivery, inspection, mapping, and monitoring. The growing importance of quadcopters is due to their ability to perform a variety of tasks, such as mapping, inspection, surveillance, and delivery. However, their design and four motors make them inherently unstable and difficult to control, which can limit their potential. To improve stability, four controllers (Proportional Derivative, Proportional Integral Derivative, Linear Quadratic Regulator, and Linear Quadratic Regulator-Proportional Derivative) were tested on a quadcopter Simulink model created with MATLAB/SIMULINK. Simulation results showed that the Linear Quadratic Regulator-Proportional Derivative controller was the most effective in terms of stabilization and speed, particularly for hovering along the x and y axes. This controller was found to be reliable and efficient, providing a smooth and fast response. Tuning these controllers can optimize the quadcopter's stability, accuracy, and speed, enabling them to perform a wider range of tasks.

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“…According to Newton's Second Law of Motion and the Coriolis Theorem which represents the angular velocity derivative. The rotational motion can be expressed by Equation 19…”

mentioning

confidence: 99%

Comparative Study of PID, PD, LQR, and LQR-PD Regulators for Quadrotor Stabilization and Trajectory Tracking

Djizi,

Zahzouh

2023

Preprint

0

View full text Add to dashboard Cite

Quadrotors, a type of unmanned aerial vehicle, utilize four rotors for precise lift and control, making them highly versatile for a variety of tasks, such as delivery, inspection, mapping, and monitoring. The growing importance of quadcopters is due to their ability to perform a variety of tasks, such as mapping, inspection, surveillance, and delivery. However, their design and four motors make them inherently unstable and difficult to control, which can limit their potential. To improve stability, four controllers (Proportional Derivative, Proportional Integral Derivative, Linear Quadratic Regulator, and Linear Quadratic Regulator-Proportional Derivative) were tested on a quadcopter Simulink model created with MATLAB/SIMULINK. Simulation results showed that the Linear Quadratic Regulator-Proportional Derivative controller was the most effective in terms of stabilization and speed, particularly for hovering along the x and y axes. This controller was found to be reliable and efficient, providing a smooth and fast response. Tuning these controllers can optimize the quadcopter's stability, accuracy, and speed, enabling them to perform a wider range of tasks.

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Proposal of a Compact Neuro-Fuzzy Adaptive Controller for Filling Regulation of Two Coupled Spherical Tanks

Espitia,

Machón,

López

2024

Int. J. Fuzzy Syst.

0

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This paper displays the set up and simulation of a compact neuro-fuzzy adaptive scheme for the filling regulation of two coupled spherical tanks. The suggested scheme employs two compact neuro-fuzzy blocks: the first one to model the plant, and the second one for the controller implementation. In this scheme, the controller is trained employing the fuzzy model estimated with data of the system working in closed-loop. Thus, the controller optimization iteratively is performed when plant variations occur. The work also includes the deduction of the equations for training, showing the adaptive process employing neuro-fuzzy systems. Moreover, the training (optimization) process of the controller’s neuro-fuzzy system includes within the adjustment function the control action and the error signal. Various experimental cases are considered using statistical analysis to verify behaviors in the adaptive control system. In this order, the main contribution of this work consists of the adjustment (coupling) of two structures of compact neuro-fuzzy systems used for identification and control, as well as the deduction and adjustment of the training algorithms to implement the adaptive control system.

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A fuzzy adaptive controller design for integrated guidance and control of a nonlinear model helicopter

Cited by 4 publications

References 17 publications

Comparative Study of PID, PD, LQR, and LQR-PD Regulators for Quadrotor Stabilization and Trajectory Tracking

Comparative Study of PID, PD, LQR, and LQR-PD Regulators for Quadrotor Stabilization and Trajectory Tracking

Comparative Study of PID, PD, LQR, and LQR-PD Regulators for Quadrotor Stabilization and Trajectory Tracking

Proposal of a Compact Neuro-Fuzzy Adaptive Controller for Filling Regulation of Two Coupled Spherical Tanks

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