Design of a Fixed-Wing UAV Controller Combined Fuzzy Adaptive Method and Sliding Mode Control

Guo, Yufei; Luo, Leru; Bao, Changchun

doi:10.1155/2022/2812671

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Other authors have studied adaptive control theory to propose solutions to achieve a stable flight with fixed-wing UAVs; for example, in [1], an adaptive controller based on fuzzy logic and sliding mode control (SMC) is designed, ref. [1] used fuzzy logic to reduce the chattering in the SMC and used the SMC methodology to approximate the uncertain parameters and unknown functions caused by the external perturbations or disturbances. Two types of fuzzy adaptive SMC for different fuzzy ranges are proposed.…”

Section: Introductionmentioning

confidence: 99%

MRAS Using Lyapunov Theory with Sliding Modes for a Fixed-Wing MAV

Espinoza-Fraire,

Saenz,

Gandarilla

et al. 2024

Applied Sciences

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This work applies an adaptive PD controller based on MRAS (Model Reference Adaptive System) using Lyapunov theory with sliding mode theory to a Fixed-wing MAV (Mini Aerial Vehicle). The objective is to design different adjustment mechanisms to obtain a robust adaptive control law in the presence of unknown perturbation due to wind gusts. Four adjustment mechanisms applied to an adaptive PD controller are compared. The adjustment mechanisms are Lyapunov theory, Lyapunov theory with first-order sliding mode, Lyapunov theory with second-order sliding mode, and Lyapunov theory with high-order sliding mode. Finally, after several simulations, a significant reduction and almost elimination of the unknown perturbations are presented with the addition of the sliding mode theory in the design of the adjustment mechanism for the adaptive PD controller.

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Section: Introductionmentioning

confidence: 99%

MRAS Using Lyapunov Theory with Sliding Modes for a Fixed-Wing MAV

Espinoza-Fraire,

Saenz,

Gandarilla

et al. 2024

Applied Sciences

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“…Many nonlinear and linear controls have therefore been established given these circumstances. For instance, adaptive control [4][5][6], backstepping control [7,8], sliding mode control [9][10][11][12][13][14], optimal control [15,16] and intelligent control [17][18][19][20][21] have been deployed.…”

Section: Introductionmentioning

confidence: 99%

“…For the stability of tri-motor UAV, this model reference adaptive control (MRAC) is utilized, which shows this algorithm has better transient performance with zero steady-state and fast convergence towards stability. To overcome the complexity of a coupled nonlinear model of a fixed-wing UAV system, [20] proposed a control algorithm combining fuzzy adaptive control and sliding mode variable structure control. The stability of the controllers was verified using the Lyapunov stability theorem.…”

Section: Introductionmentioning

confidence: 99%

Position and Attitude Tracking of MAV Quadrotor Using SMC-Based Adaptive PID Controller

Noordin

Basri

Mohamed

2022

Drones

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A micro air vehicle (MAV) is physically lightweight, such that even a slight perturbation could affect its attitude and position tracking. To attain better autonomous flight system performance, MAVs require good control strategies to maintain their attitude stability during translational movement. However, the available control methods nowadays have fixed gain, which is associated with the chattering phenomenon and is not robust enough. To overcome the aforementioned issues, an adaptive proportional integral derivative (PID) control scheme is proposed. An adaptive mechanism based on a second-order sliding mode control is used to tune the parameter gains of the PID controller, and chattering phenomena are reduced by a fuzzy compensator. The Lyapunov stability theorem and gradient descent approach were the basis for the automated tuning. Comparisons between the proposed scheme against SMC-STA and SMC-TanH were also made. MATLAB Simulink simulation results showed the overall favourable performance of the proposed scheme. Finally, the proposed scheme was tested on a model-based platform to prove its effectiveness in a complex real-time embedded system. Orbit and waypoint followers in the platform simulation showed satisfactory performance for the MAV in completing its trajectory with the environment and sensor models as perturbation. Both tests demonstrate the advantages of the proposed scheme, which produces better transient performance and fast convergence towards stability.

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