Neural network trajectory tracking of tracked mobile robot

Asai, Madoka; Chen, Gan; Takami, Isao

doi:10.1109/ssd.2019.8893152

Cited by 15 publications

(4 citation statements)

References 9 publications

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“…As a basic problem for the application of the WMR, trajectory tracking control has been widely investigated and many control methods have been applied in this area, such as the neural network (Asai et al, 2019; Chen et al, 2020; Yuan et al, 2014), adaptive control (Ayten et al, 2019; Boukens et al, 2017), active disturbance rejection control (Chen et al, 2018), sliding mode control (Mera et al, 2020; Yang et al, 2018), fuzzy logic control (Abdelwahab et al, 2020; Akka and Khaber, 2019), feedback linearization (Chwa, 2010; Montoya-Villegas et al, 2019), model predictive control (Wang et al, 2018; Yang et al, 2018) and so on.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control for wheeled mobile robot subject to generalized torque constraints

Shen

Bai

Shi³

et al. 2022

Transactions of the Institute of Measurement and Control

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The trajectory tracking control problem for wheeled mobile robot (WMR) subject to generalized torque constraints is studied in this paper. First, a more general model is obtained by taking the displacement between the centre of mass of the WMR and the midpoint of the driving wheels into consideration. Then, a virtual velocity controller and a generalized torque controller are designed based on a new Lyapunov function. Due to the advantages of the new Lyapunov function, the stability of the trajectory tracking error system can be derived directly and the constraint of the initial tracking errors is removed. Less conservative conditions for tuning the controller parameters to satisfy the saturation constraints are also derived. Finally, some numerical examples are given to show the effectiveness of the proposed controllers.

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Section: Introductionmentioning

confidence: 99%

Trajectory tracking control for wheeled mobile robot subject to generalized torque constraints

Shen

Bai

Shi³

et al. 2022

Transactions of the Institute of Measurement and Control

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show abstract

“…The world is filled with lots of data in the form of pictures, texts, audio files, etc. Machine learning is being used for deriving meaning from all that data by classifying, detecting [1], [2] and tracking [3] the data. Convolutional Neural Networks (CNN) is most commonly used and is known for handling huge unstructured data.…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of AAD pooling unit and performance analysis

Kiran¹,

Rao²,

Sudheer³

et al. 2022

World J. Adv. Res. Rev.

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Convolutional Neural Network (CNN) has been witnessing a massive growth for its various applications in different fields. It is a category of Neural Network or Deep learning that is being used in text detection, image classification, etc. It is also very effective for the classifications which are non-image like audio classifications, signal data classifications, etc. CNN is composed of convolutional layer, pooling layer and finally the fully connected layer. In this work, we mainly focus on pooling layer which impacts accuracy and speed of CNN. This work implements validation of CNN based CIFAR-10 classifier and Field Programmable Gate Array (FPGA) implementation of Absolute Average Deviation (AAD) Pooling unit. Pooling techniques like max pooling, average pooling, mixed pooling, min pooling, etc., are currently being used. This work uses AAD pooling in CNN to support that this pooling is having higher accuracy, i.e., 89% and less computational complexity. A benchmark CNN structure using TensorFlow is adopted to quantify the performance of the AAD pooling unit. Further, Register Transfer Level (RTL) coding is done in Verilog HDL and the testbench is developed. The FPGA implementation is carried out using the Xilinx Vivado tool.

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“…Vehicle dynamics, rough terrain, skid steering, and nonholonomic constraints have been taken into account by the proposed controller. In Asai et al, 26 an online neural network (NN) methodology is developed for trajectory tracking of small-tracked. Where three binary sensors are used in this work to obtain the vehicle location with respect to the desired trajectory.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling and optimized trajectory tracking control of an autonomous tracked vehicle via backstepping and sliding mode control

Sabiha

Kamel

Said

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a complete kinematic and dynamic modeling and trajectory tracking control of an autonomous tracked vehicle. First, based on the vehicle’s kinematics, the reference linear and angular velocities are evaluated. The kinematic controller is proposed as an integrated backstepping controller. Then, based on vehicle dynamics and slipping characteristics, an integral sliding mode control is utilized to get the desired vehicle drive torques and converge its trajectory to the desired one. Whereas the controller gains are optimally calculated. Furthermore, based on the Lyapunov stability, the proof of stability for proposed controllers is presented. Finally, simulation results are conducted to validate the effectiveness of the proposed control algorithm compared with a hybrid backstepping-modified proportional–integral–derivative dynamic controller and a nonlinear feedback acceleration controller.

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Neural network trajectory tracking of tracked mobile robot

Cited by 15 publications

References 9 publications

Trajectory tracking control for wheeled mobile robot subject to generalized torque constraints

Trajectory tracking control for wheeled mobile robot subject to generalized torque constraints

FPGA implementation of AAD pooling unit and performance analysis

Dynamic modeling and optimized trajectory tracking control of an autonomous tracked vehicle via backstepping and sliding mode control

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