UAV quadrotor attitude control: An ADRC-EMC combined approach

Lotufo, Mauricio Alejandro; Colangelo, Luigi; Montenegro, Carlos Perez; Canuto, Enrico; Novara, Carlo

doi:10.1016/j.conengprac.2018.11.002

Cited by 81 publications

(40 citation statements)

References 11 publications

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“…In this work, different from our previous proposals [37], we use the BsNN for calculating the control gains in indirect way, it means by computing the control parameters introduced in (17). In this fashion, we try to constrain the BsNN whilst the Hurwitz stability condition keeps.…”

Section: Input Vector Basis Functionsmentioning

confidence: 99%

“…The performance of the control is ensured by fixing a reference model of the tracking error. Meanwhile, in [17] a framework of Active Disturbance Rejection Control (ADRC) and Embedded Model Control (EMC) is proposed for robust attitude control of a four-rotor helicopter, where an state predictor, a control law, and a model-based reference generator are designed.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neural Motion Control of a Quadrotor UAV

Yañez-Badillo¹,

Tapia‐Olvera

Beltrán-Carbajal

2020

Vehicles

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Unmanned Aerial Vehicles have generated considerable interest in different research fields. The motion control problem is among the most important issues to be solved since system dynamic stability depends on the robustness of the main controller against endogenous and exogenous disturbances. In spite of different controllers have been introduced in the literature for motion control of fixed and rotary wing vehicles, there are some challenges for improving controller features such as simplicity, robustness, efficiency, adaptability, and stability. This paper outlines a novel approach to deal with the induced effects of external disturbances affecting the flight of a quadrotor unmanned aerial vehicle. The aim of our study is to further extend the current knowledge of quadrotor motion control by using both adaptive and robust control strategies. A new adaptive neural trajectory tracking control strategy based on B-spline artificial neural networks and on-line disturbance estimation for a quadrotor is proposed. A linear extended state observer is used for estimating time-varying disturbances affecting the controlled nonlinear system dynamics. B-spline artificial neural networks are properly synthesized for on-line calculating control gains of an adaptive Proportional Integral Derivative (PID) scheme. Simulation results highlight the implementation of such a controller is able to reject disturbances meanwhile perform proper motion control by exploiting the robustness, disturbance rejection, adaptability, and self-learning capabilities.

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Section: Input Vector Basis Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Motion Control of a Quadrotor UAV

Yañez-Badillo¹,

Tapia‐Olvera

Beltrán-Carbajal

2020

Vehicles

View full text Add to dashboard Cite

show abstract

“…ird is the study of flight path prediction. According to the available data, the method of graded management of accident factors and the scheme of risk assessment have been initially established [16]. Chen studied early accident diagnosis and provided research ideas for parameter selection and evaluation [17], Hu studied the defects of accident diagnosis and proposed the use of multiplicity selection method to carry out simulation experiments [18], and Zhang et al established a method to show the application effect of experimental results by using Image Dehazing (ID) technology [19,20].…”

Section: Introductionmentioning

confidence: 99%

An Operational Safety Evaluation Method for Manned Transport Aircraft and Large UAV in Mixed Airspace

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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To improve the predictive ability in trajectory of large unmanned aerial vehicle (UAV) and the calculation performance in complicated circumstances with mixed airspace, multiple aircraft types, and joint operations, the concept of phased trajectory deviation (PTD) is introduced and a corresponding minimal interval algorithm (PTD-MI) is set up. This algorithm is capable of deriving the minimal interval between various aircraft types according to the crosswind impacts and the UAV characteristics at different flight phases and thus achieves the effective safety evaluation in airspace operation. To demonstrate the rationality and generality of the proposed algorithm, several simulation experiments are conducted. Based on the experimental results, flight procedure protection area is plotted by PTD-MI algorithm and compared with that generated by Ground-Based Augmentation System (GBAS). Results indicate that the proposed algorithm is capable of deriving a more scientific basis for airspace assignment and outperform GBAS in dealing with wide-area space problems. And, compared with GBAS, PTD-MI algorithm shows a more stable calculation performance and is easier to output the results. PTD-MI algorithm is proposed under the flight safety regulation designed by the International Civil Aviation Organization (ICAO) and designed to provide effective technical supports for the safe and normal operations of aircrafts.

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“…In other fields, ADRC also shows great control effect. ADRC and Embedded Model Control (EMC) were combined to estimate the disturbances and uncertainties affecting unmanned aerial vehicles (UAV) and eliminate them online [14]. For the powered parafoil, Luo et al proposed an accurate flight-path-tracking control approach combining ADRC and wind feedforward compensation, which can achieve better tracking performance and robustness against the variable wind disturbance [15].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

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The control issue of Autonomous Underwater Vehicles (AUV) is very challenging since the precise mathematical model of AUV is hard to establish due to its strong coupling and time-varying features. Meanwhile, AUV movement is easily interfered with by ocean currents and waves, causing anti-interference performance of traditional Proportional-Integral-Derivative (PID) control to be unsatisfactory. Aiming to solve those problems, an algorithm of fuzzy optimized model-free adaptive control (MFAC) based on auto-disturbance rejection control (ADRC) was proposed and used in AUV heading control. The MFAC is used to overcome the difficulty with establishing a precise mathematical model, and the ADRC is introduced to handle the interference of currents and waves. In this paper, MFAC and ADRC are combined. First, the MFAC is performed based only on the I/O data of the controlled object, which is simple to implement with low calculation complexity and strong robustness. Then, a tracking differentiator (TD) is employed to track the input signal to overcome the antinomy of rapidity and hypertonicity in MFAC. After that, an extended-state observer (ESO) is added to control the variables of MFAC to estimate all the disturbances, which can greatly improve the anti-interference ability of the system. Due to the complexity and diversity of the marine environment, a fuzzy optimized MFAC based on ADRC is proposed to improve the adaptability of AUV to the marine environment. Simulations and experiments were carried out to verify the control effect of this algorithm in complex sea conditions.

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UAV quadrotor attitude control: An ADRC-EMC combined approach

Cited by 81 publications

References 11 publications

Adaptive Neural Motion Control of a Quadrotor UAV

Adaptive Neural Motion Control of a Quadrotor UAV

An Operational Safety Evaluation Method for Manned Transport Aircraft and Large UAV in Mixed Airspace

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

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