Adaptive Fuzzy Output Feedback Simultaneous Posture Stabilization and Tracking Control of Wheeled Mobile Robots With Kinematic and Dynamic Disturbances

Chwa, Dongkyoung; Boo, Junseok

doi:10.1109/access.2020.3046282

Cited by 16 publications

(13 citation statements)

References 43 publications

(74 reference statements)

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“…Pada artikel [9] pembahasan mengenai penggunaan sensor odometry pada robot beroda, TRAJECTORY TRACKING ROBOT DIFFERENTIAL DRIVE dimana dapat disimpulkan bahwa penggunaan sensor odometry saja memiliki data yang kurang akurat dikarenakan adanya selip pada ban robot, kemudian pada artikel [10] yang membahas kurang akuratnya penggunaan sensor laser pada tracking posisi robot dikarenakan sensor laser yang hanya mampu bekerja secara 2 dimensi saja, kemudian pada artikel [11] yang meneliti penggunaan sensor ultrasonik sebagai sensor tracking posisi robot yang hasilnya kurang akurat karena sensor ultrasonik memiliki banyak titik buta pada pemasangannya. Serta artikel [12] yang membahas mengenai penggunaan sensor kamera yang memiliki data yang bagus, namun memiliki cost harga yang sangat tinggi.…”

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Implementasi g-h Filter Pada Sensor Kompas Sebagai Peningkatan Akurasi Trajectory Tracking Robot Differential Drive

2022

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Proses trajectory tracking pergerakan robot differential drive sangatlah bergantung pada data sensor yang digunakan, dimana umumnya menggunakan sensor odometri yang sangat rentan akurasinya. Dengan menambahkan sensor lain merupakan salah satu upaya dalam meningkatkan akurasi algoritma tersebut, salah satunya adalah dengan sensor kompas. Namun noise data yang sangat tinggi yang dimiliki sensor kompas, berakibat diperlukannya filter untuk memperbaiki data sensor tersebut, dengan tujuan agar hasil proses trajectory tracking dapat lebih akurat. Salah satunya dengan g-h filter. Penelitian dilakukan dengan mensimulasikan data kompas yang memiliki noise yang sangat tinggi sebagai input dari g-h filter dimana output data filternya nanti dijadikan input data dari kontrol kinematik dari robot differential drive. Dalam penggunaan g-h filter dibutuhkan proses tuning pada parameter g dan h yang optimal, sehingga mampu menghasilkan output yang sesuai. Dari hasil simulasi pengujian g-h filter didapatkan nilai konstanta g sebesar 0.56 dan konstanta h sebesar 0.12 yang memiliki output data yang untuk menghilangkan data noise dari sensor kompas sehingga mampu meningkatkan akurasi data dari trajectory tracking pergerakan robot beroda, terbukti dari hasil pengujian yang menunjukkan nilai rata – rata error data dibawah 0.02 meter.

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Implementasi g-h Filter Pada Sensor Kompas Sebagai Peningkatan Akurasi Trajectory Tracking Robot Differential Drive

2022

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“…Firstly, the following nonlinear sliding surfaces s p and s θ constructed by tracking errors are designed. VOLUME 10, 2022 Here, the sliding surface s p corresponds to trajectory tracking and s θ corresponds to robot's heading tracking represented as s p = ėp + k p e p + f p sign(e p ) s θ = ėθ + k θ e θ + f θ sign(e θ ) (16) where k p , k θ , f p , and f θ are positive constants and sign(•) is a signum function [13], [15], [21], [26], [29], [31], [32], [35], [36]. Eq.…”

Section: ) Nonlinear Sliding Surfaces Designmentioning

confidence: 99%

LiDAR Based Trajectory-Tracking of an Autonomous Differential Drive Mobile Robot Using Fuzzy Sliding Mode Controller

Zaman

2022

IEEE Access

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This study aims to achieve the trajectory-tracking of an autonomous differential drive mobile robot (ADDMR) in the presence of friction torques using the proposed Fuzzy Sliding-Mode Control (FSMC). First, the complete model of the ADDMR is established including kinematic, dynamic and actuator models. Then, the desired trajectory is planned and generated via Bézier curve combined with cubic time mapping. The localization of the ADDMR is performed using the Monte Carlo Localization (MCL) technique and LiDAR point cloud data. Subsequently, the proposed FSMC utilizes nonlinear sliding surfaces inferred based on fuzzy logic to asymptotically attain the pose convergence. To investigate the feasibility and robustness of the proposed FSMC, simulations and experiments are conducted under different operation conditions. The results show that the proposed FSMC using LiDAR data in the feedback leads to excellent control performance even in the presence of friction torques. It was also shown through simulations and experiments that the established ADDMR model fits the actual ADDMR model very well.

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“…Fuzzy logic control (FLC) presents an advantageous alternative in these circumstances because it limits the inputs within the range of the predefined membership functions as demonstrated in refs. [25][26][27][28][29][30][31][32][33][34][35]. The membership functions are typically designed to overlap with one another.…”

Section: Introductionmentioning

confidence: 99%

“…This approach provides interpolative reasoning capabilities as highlighted in refs. [25][26][27][28] where the FLC shows a high resilience against uncertainties and disturbances. Furthermore, as indicated in refs.…”

Section: Introductionmentioning

confidence: 99%

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

Zaman,

2024

Robotica

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In this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.

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Adaptive Fuzzy Output Feedback Simultaneous Posture Stabilization and Tracking Control of Wheeled Mobile Robots With Kinematic and Dynamic Disturbances

Cited by 16 publications

References 43 publications

Implementasi g-h Filter Pada Sensor Kompas Sebagai Peningkatan Akurasi Trajectory Tracking Robot Differential Drive

Implementasi g-h Filter Pada Sensor Kompas Sebagai Peningkatan Akurasi Trajectory Tracking Robot Differential Drive

LiDAR Based Trajectory-Tracking of an Autonomous Differential Drive Mobile Robot Using Fuzzy Sliding Mode Controller

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

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