Practical finite time adaptive robust flight control system for quad-copter UAVs

Eliker, Karam; Grouni, Said; Tadjine, Mohamed; Zhang, Weidong

doi:10.1016/j.ast.2020.105708

Cited by 42 publications

(14 citation statements)

References 63 publications

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“…Lemma 1 [35], [37]: For a system ṡ(t) = g(s(t)), suppose that a positive-definite function X (s) meets Ẋ(s) ≤ −κ 1 X (s) − κ 2 X α (s), where κ 1 > 0, κ 2 > 0, 0 < α < 1. Then, the system ṡ(t) = g(s(t)) is finite-time stable, and the ST is…”

Section: B Definitions and Lemmasmentioning

confidence: 99%

The Optimization of Control Parameters: Finite-Time Bipartite Synchronization of Memristive Neural Networks With Multiple Time Delays via Saturation Function

Chang

Park²,

Yang³

2023

IEEE Trans. Neural Netw. Learning Syst.

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This article studies the memristive neural networks with multiple time delays (MNNsMTDs). The topology of networks is signed, which contains both cooperative and competitive relationships. Two controllers without time delays are designed to achieve finite-time bipartite synchronization (FTBS) and practical FTBS (PFTBS) of MNNsMTDs. A novel controller with a saturation function rather than a sign function is proposed to avoid chattering. Along with the Lyapunov function method, some mathematical techniques, and scaling inequalities, some sufficient conditions for FTBS and PFTBS of MNNsMTDs are attained. Besides, this article also concerns fixed-time bipartite synchronization (FXBS) and practical FXBS (PFXBS) of MNNsMTDs. An optimization model is designed to obtain some optimal control parameters. An algorithm based on particle swarm optimization (PSO) is provided to solve this model. Some numerical examples are included to demonstrate the correctness and applicability of the approaches.

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Section: B Definitions and Lemmasmentioning

confidence: 99%

The Optimization of Control Parameters: Finite-Time Bipartite Synchronization of Memristive Neural Networks With Multiple Time Delays via Saturation Function

Chang

Park²,

Yang³

2023

IEEE Trans. Neural Netw. Learning Syst.

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“…Hence, _ V 2 is negative definite, which implies that s x,1 and ϵ x,1 will converge to zero equilibrium. Therefore, when the states are confined on the sliding manifold after the reaching phase, it turns that the position error e x to approach the 12 International Journal of Aerospace Engineering origin in a finite time during the sliding phase, i.e., when s x,1 → 0 and _ s x,1 → 0. This completes the proof.…”

Section: Theorem 18mentioning

confidence: 99%

“…Literature Review and Contributions. Recently, a large and growing body of literature has been focused on the Cartesian trajectory tracking control problem of the quadrotor system using finite-time control, as stated in [12,13]. In contrast to classical asymptotically stable controllers, finite-time stable control systems ensure fast convergence of the system's trajectories to the origin along with higher accuracy and enhanced robustness.…”

Section: Introductionmentioning

confidence: 99%

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Robust Finite-Time Trajectory Tracking Control of Quadrotor Aircraft via Terminal Sliding Mode-Based Active Antidisturbance Approach: A PIL Experiment

Mechali

Iqbal

Xie

et al. 2021

International Journal of Aerospace Engineering

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This paper presents an accurate solution of finite-time Cartesian trajectory tracking control problem of a quadrotor system by designing and implementing a novel robust flight-control algorithm. The quadrotor is subject to nonlinearities, unmodeled dynamics, parameters’ uncertainties, and external time-varying disturbances. To reject the disturbances and enhance the control system’s robustness, a terminal sliding mode-based active antidisturbance control (TSMBAADC) approach is proposed for rotational and translational subsystems. To improve the tracking performance, a nonlinear continuous terminal sliding manifold and a fast reaching law are proposed in this work to quickly drive the systems’ states to the equilibrium point even in the presence of lumped disturbances. The convergence time of the states can be pretuned based on the parameters of the sliding manifold and the reaching law. Lyapunov theorem is used to provide a rigorous stability proof for the feedback control system. Numerical simulations and processor-in-the-loop (PIL) experiments are conducted to validate and implement the designed flight control algorithm on real autopilot hardware. The novelty of the proposed research lies in hardware implementation of a sophisticated version of modern control technique that exhibits a multitude of distinguishing features including but not limited to (i) finite-time tracking stability featuring fast convergence is ensured, (ii) chattering and singularity problems in sliding mode control (SMC) are avoided, and (iii) null steady-state error is achieved along with enhanced robustness. Finally, the proposed control law is compared with two recently reported research works. Results of performance comparison in term of the integral of square error (ISE) and the absolute value of the derivative of the input u t (IADU) dictate that the proposed technique overperforms by precision and chattering alleviation.

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“…In addition, drones are constantly enriching people's lives and leading to the modification of industry models. Though drone designers have introduced the high robust flight control system [2,3], intelligent cooperative scheme [4,5], collision avoidance algorithms [6,7] and collision avoidance systems [8,9] to drones, collision safety issues are inevitable for the restrictions on the reliability of the drone itself and the professionalism of the operators.…”

Section: Introductionmentioning

confidence: 99%

A Simplified FE Modeling Strategy for the Drop Process Simulation Analysis of Light and Small Drone

Zhang

Huang

et al. 2021

Aerospace

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The numerical accuracy of drop process simulation and collision response for drones is primarily determined by the finite element modeling method and simplified method of drone airframe structure. For light and small drones exhibiting diverse shapes and configurations, mixed materials and structures, deformation and complex destruction behaviors, the way of developing a reasonable and easily achieved high-precision simplified modeling method by ensuring the calculation accuracy and saving the calculation cost has aroused increasing concern in impact dynamics simulation. In the present study, the full-size modeling and simplified modeling methods that are specific to different components of a relatively popular light and small drone were analyzed in an LS-DYNA software environment. First, a full-size high-precision model of the drone was built, and the model accuracy was verified by performing the drop tests at the component level as well as the whole machine level. Subsequently, based on the full-size high-precision model, the property characteristics of the main components of the light and small drone and their common simplification methods were classified, a series of simplified modeling methods for different components were developed, several single simplified models and combined simplified models were built, and a method to assess the calculation error of the peak impact load in the simplified models was proposed. Lastly, by comparing and analyzing the calculation accuracy of various simplified models, the high-precision simplified modeling strategy was formulated, and the suggestions were proposed for the impact dynamics simulation of the light and small drone falling. Given the analysis of the calculation scale and solution time of the simplified model, the high-precision simplified modeling method developed here is capable of noticeably reducing the modeling difficulty, the solution scale and the calculation time while ensuring the calculation accuracy. Moreover, it shows promising applications in several fields (e.g., structure design, strength analysis and impact process simulation of drone).

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Practical finite time adaptive robust flight control system for quad-copter UAVs

Cited by 42 publications

References 63 publications

The Optimization of Control Parameters: Finite-Time Bipartite Synchronization of Memristive Neural Networks With Multiple Time Delays via Saturation Function

The Optimization of Control Parameters: Finite-Time Bipartite Synchronization of Memristive Neural Networks With Multiple Time Delays via Saturation Function

Robust Finite-Time Trajectory Tracking Control of Quadrotor Aircraft via Terminal Sliding Mode-Based Active Antidisturbance Approach: A PIL Experiment

A Simplified FE Modeling Strategy for the Drop Process Simulation Analysis of Light and Small Drone

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