Finite‐time disturbance observer‐based trajectory tracking control for quadrotor unmanned aerial vehicle with obstacle avoidance

Li, Bo; Zhang, Haichao; Niu, Yuxiao; Ran, Dechao; Xiao, Bing

doi:10.1002/mma.8567

Cited by 16 publications

(9 citation statements)

References 44 publications

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“…Two distinct desired trajectories are employed. Refer to the article of Li [31], where one trajectory (Path I) is a typical sinusoidal path:…”

Section: Parameter Selectionmentioning

confidence: 99%

USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning

Wen,

Chen,

Guo

2024

Sensors

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We present a novel approach for achieving high-precision trajectory tracking control in an unmanned surface vehicle (USV) through utilization of receding horizon reinforcement learning (RHRL). The control architecture for the USV involves a composite of feedforward and feedback components. The feedforward control component is derived directly from the curvature of the reference path and the dynamic model. Feedback control is acquired through application of the RHRL algorithm, effectively addressing the problem of achieving optimal tracking control. The methodology introduced in this paper synergizes with the rolling time domain optimization mechanism, converting the perpetual time domain optimal control predicament into a succession of finite time domain control problems amenable to resolution. In contrast to Lyapunov model predictive control (LMPC) and sliding mode control (SMC), our proposed method employs the RHRL controller, which yields an explicit state feedback control law. This characteristic endows the controller with the dual capabilities of direct offline and online learning deployment. Within each prediction time domain, we employ a time-independent executive–evaluator network structure to glean insights into the optimal value function and control strategy. Furthermore, we substantiate the convergence of the RHRL algorithm in each prediction time domain through rigorous theoretical proof, with concurrent analysis to verify the stability of the closed-loop system. To conclude, USV trajectory control tests are carried out within a simulated environment.

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“…Two distinct desired trajectories are employed. Refer to the article of Li [31], where one trajectory (Path I) is a typical sinusoidal path:…”

Section: Parameter Selectionmentioning

confidence: 99%

USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning

Wen,

Chen,

Guo

2024

Sensors

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“…Literature [23] proposes a new multi-hop opportunity 3D router (MO3RD) algorithm for post-disaster data transmission, and simulation experiments confirm the feasibility of the closed-resolution model, which enables UAV operations to satisfy coverage constraints and collision constraints within the threshold distance van between UAVs while maximizing data transmission. Literature [24] explores a new sliding mode position tracking control scheme based on surface-like variables and an attitude synchronization control scheme based on non-singular terminal sliding modes to enable UAV obstacle avoidance without losing trajectory. The effectiveness of this control scheme is verified through the use of the Liapunov method analysis.…”

Section: Introductionmentioning

confidence: 99%

Study on dynamic collision avoidance during UAV inspection based on improved graph theory network algorithm

Li,

Lan,

Liang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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With the development of the times, UAVs are gradually promoted and popularized in military and civil fields, and the future airspace will also face more security risks. This paper combines the graph theory network algorithm to plan the initial path of UAV inspection and completes the dynamic collision avoidance path planning in the process of UAV inspection through the established joint model of UAV inspection sensing and avoidance. At the same time, the ant colony algorithm is introduced to improve the graph theory network algorithm to solve the dynamic collision avoidance optimal path in the process of UAV inspection. On this basis, simulation experiments of path planning design for UAV collision avoidance and kinematic simulation with two-fold priority judgment are carried out, and the kinematic parameters corresponding to the collision avoidance path are selected as the analysis anchor points. The extreme values of horizontal speed, climb speed, trajectory inclination angular rate, and heading angular rate are 31.6 m/s, 3 m/s, 5.7°/s, and 18.4°/s, respectively, which are within the given constraints, and verify the reasonableness and effectiveness of the proposed optimization scheme. The average time of the proposed algorithm is 0.0458s, which is much lower than the corresponding 0.7105s of the original scheme algorithm, through data comparison of simulation experiments and experimental validation. Using a graph theory network algorithm, the proposed optimization scheme is more efficient and stable and has a higher success rate for collision avoidance.

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“…In contrast to controllers with asymptotic stability, the finitetime control methods may offer fast convergence and higher tracking accuracy in the face of environmental disturbances [36]. In [37], a finite-time disturbance-based controller using SMC has been utilized for quadrotor obstacle avoidance. In [38], a robust finite-time controller has been developed for a quadrotor.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping and Sliding Mode Control of a Quadrotor

maaruf,

Gulzar,

Abubakar

2024

Preprint

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Quadrotors are increasingly employed for both civilian and military applications. Recently, researchers have combined different control and estimation schemes to come up with a hybrid control structure to improve the robustness and tracking performance of the quadrotors. To further enhance the tracking precision of quadrotors subjected to parametric variations and environmental disturbances, this article proposes a new robust adaptive hybrid control architecture. In this study, the quadrotor model is divided into altitude, attitude, and position subsystems for which appropriate control methods are designed. A fractional-order sliding mode control with adaptive gain (AFSMC) is designed to enhance the tracking of the altitude subsystem. A robust backstepping control with adaptive gain (RAB) is developed for the horizontal position to generate the required roll and pitch orientations. A nonsingular fast terminal sliding mode control (NFTSMC) is incorporated with a finite-time disturbance observer (FDO) to accurately suppress the disturbances, follow the target rotation angles, and attain finite-time stability. The compounded control structure ensures accurate, fast, and robust tracking. The efficacy of the developed hybrid control scheme is assessed via simulations and comparisons with existing control methods.

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Finite‐time disturbance observer‐based trajectory tracking control for quadrotor unmanned aerial vehicle with obstacle avoidance

Cited by 16 publications

References 44 publications

USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning

USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning

Study on dynamic collision avoidance during UAV inspection based on improved graph theory network algorithm

Adaptive Backstepping and Sliding Mode Control of a Quadrotor

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