Control of Quadrotor using a Neural Network based PID

Gómez-Avila, Javier; López-Franco, Carlos; Alanis, Alma Y.; Arana‐Daniel, Nancy

doi:10.1109/la-cci.2018.8625222

Cited by 14 publications

(4 citation statements)

References 12 publications

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“…For tuning PID parameters, several offline methods are available. In this section, the critical ideas of ANN, supervised learning and system identification methodologies are introduced and analyzed in relation to the theoretical development and applications of optimal PID controllers [27]. ANNs can offer flexible solutions to non-linear problems with their structure similar to the human brain.…”

Section: Proposed Artificial Neural Network (Ann)mentioning

confidence: 99%

Tuning the Proportional–Integral–Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach

Ulu,

Savaş,

Ergin

et al. 2024

Sensors

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Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional–integral–derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10−5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.

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Section: Proposed Artificial Neural Network (Ann)mentioning

confidence: 99%

Tuning the Proportional–Integral–Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach

Ulu,

Savaş,

Ergin

et al. 2024

Sensors

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show abstract

“…Unmanned Aerial Vehicle (UAV) diartikan sebagai wahana terbang tanpa awak yang dapat terbang dengan dikendalikan secara jarak jauh maupun menggunakan kendali otomatis. Kemudahan tersebut membuat UAV banyak digunakan dalam berbagai hal [1]. Salah satu jenis UAV yang sering digunakan saat ini adalah quadrotor [2].…”

Section: Pendahuluanunclassified

“…Kendali PID atau PD banyak digunakan oleh dunia industri karena kemudahan dalam penggunaannya [1]. Kendali PID (PID controller) adalah sebuah mekanisme kendali loop feedback yang banyak digunakan dalam dunia sistem kendali.…”

Section: Pendahuluanunclassified

Model Penahan Ketinggian Quadrotor Berbasis PID dengan Jaringan Syaraf Tiruan Propagasi Mundur

Rahani

Yulianto

2021

JNTETI

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Quadrotor adalah salah satu jenis Unmanned Aerial Vehicle (UAV) atau wahana terbang tanpa awak yang dapat terbang dengan kendali jarak jauh maupun menggunakan kendali otomatis. Dalam melakukan misinya, quadrotor memerlukan sistem kendali yang baik. Salah satu sistem kendali dalam sistem quadrotor adalah sistem kendali ketinggian. Kendali ketinggian akan mengendalikan quadrotor seusai ketinggian yang diinginkan walaupun terdapat gangguan dan beban quadrotor itu sendiri. Metode kendali yang banyak digunakan adalah kendali PID. Kendali PID menghasilkan respons yang kurang baik karena konstanta PID yang bersifat tetap, sedangkan gangguan saat quadrotor terbang akan berubah-ubah. Oleh karena itu, makalah ini menawarkan kendali yang dapat menyesuaikan diri saat terkena gangguan tertentu. Metode yang ditawarkan adalah kendali PID dengan Jaringan Saraf Tiruan (JST). Sistem JST akan menala komponen PID secara real-time sesuai gangguan yang terjadi. Penggunaan PID dengan JST menghasilkan respons rise time lebih cepat 0,0594 detik, overshoot turun 7,58%, steady state error turun ±0,0672, dan settling time turun 1,031 detik dibandingkan dengan PID konvensional. Hal ini menunjukkan bahwa PID dengan JST menghasilkan respons kendali yang lebih baik dibandingkan dengan PID saja.

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“…The application of ANN for control of a wide range of dynamic systems is an actual area of research. In [2], [3], [4], authors use Radial Basis Function Neural Network (RBFNN) together with a common sliding mode and PID controller in UAVs, where the NN helps to deal with the nonlinearities, getting good performance tracking in both simulations and real tests. On the other hand, in [5], [6], [7], a different RBFNN approach is applied in a quadrotor, where the NN is designed and trained with the main purpose of estimating uncertainties and disturbance moments.…”

Section: Introductionmentioning

confidence: 99%

Transition control of a tail-sitter UAV using recurrent neural networks

Flores

2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents the implementation of a Recurrent Neural Network (RNN) based-controller for the stabilization of the flight transition maneuver (hover-cruise and vice versa) of a tail-sitter UAV. The control strategy is based on attitude and velocity stabilization. For that aim, the RNN is used for the estimation of high nonlinear aerodynamic terms during the transition stage. Then, this estimate is used together with a feedback linearization technique for stabilizing the entire system. Results show convergence of linear velocities and the pitch angle during the transition maneuver. To analyze the performance of our proposed control strategy, we present simulations for the transition from hover to cruise and vice versa.

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Control of Quadrotor using a Neural Network based PID

Cited by 14 publications

References 12 publications

Tuning the Proportional–Integral–Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach

Tuning the Proportional–Integral–Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach

Model Penahan Ketinggian Quadrotor Berbasis PID dengan Jaringan Syaraf Tiruan Propagasi Mundur

Transition control of a tail-sitter UAV using recurrent neural networks

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