Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller

Ahmed, Sevil; Petrov, Michail

doi:10.1016/j.ifacol.2015.12.071

Cited by 21 publications

(18 citation statements)

References 6 publications

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“…The geometry and kinematic parameters of the differential mobile robot is defined. The position/ orientation vector of the mobile robot and its speed are, respectively [1][2][3][4][5][6][7][8][28][29][30][31][32][33]:…”

Section: The Geometry and Kinematic Of A Mobile Robotmentioning

confidence: 99%

“…Therefore, it is also necessary to have a constant acceleration ratio, while having the same time for variable velocity in the interval of constant velocity. Therefore, in the interval of variable velocity, the acceleration ratios of both wheels must be equal to the velocity ratios of both wheels in the same interval [17][18][19][20]29,31].…”

Section: The Design Of Fuzzy Gain Scheduling Of the Pd Controllermentioning

confidence: 99%

“…This section, fuzzy gain scheduling of PD controller was installed in each of the wheels for velocity control. The following Figure shows the structure of the controller used herein [18][19][20][21]31].…”

Section: The Design Of Fuzzy Gain Scheduling Of the Pd Controllermentioning

confidence: 99%

“…Therefore, such problems can be solved through the selection of a suitable curvature and application of a gradual fuzzy gain scheduling of PD control (FGS-PD) control process based on the relationship between the current location of the mobile robot and the target points. Moreover, by controlling the velocities of each of the two wheels attached to the mobile robot through a FGS-PD controller, it is possible to compensate for the slip phenomenon based on the inertial and centrifugal forces acting on the mobile robot as it moves, while minimizing the estimation errors [26][27][28][29][30][31][32][33]. For validating the proposed method, the functions are evaluated by conducting an experiment on the estimation of the sound sources moving in real time in two different situations (straight trajectory and S-curved trajectory) [1][2][3][4][5][6][7][8][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Han

2019

Electronics

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This paper proposes fuzzy gain scheduling of proportional differential control (FGS-PD) system for tracking mobile robot to moving sound sources. Given that the target positions of the real-time moving sound sources are dynamic, the mobile robots should be able to estimate the target points continuously. In such a case, the robots tend to slip owing to abnormal velocities and abrupt changes in the tracking path. The selection of an appropriate curvature along which the robot follows a sound source makes it possible to ensure that the robot reaches the target sound source precisely. For enabling the robot to recognize the sound sources in real time, three microphones are arranged in a straight formation. In addition, by applying the cross correlation algorithm to the time delay of arrival base, the received signals can be analyzed for estimating the relative positions and velocities of the mobile robot and the sound source. Even if the mobile robot is navigating along a curved path for tracking the sound source, there could be errors due to the inertial and centrifugal forces resulting from the motion of the mobile robot. Velocities of both robot wheels are controlled using FGS-PD control to compensate for slippage and to minimize tracking errors. For experimentally verifying the efficacy of the proposed the control system with sound source estimation, two mobile robots were fabricated. It was demonstrated that the proposed control method effectively reduces the tracking error of a mobile robot following a sound source.

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Section: The Geometry and Kinematic Of A Mobile Robotmentioning

confidence: 99%

Section: The Design Of Fuzzy Gain Scheduling Of the Pd Controllermentioning

confidence: 99%

Section: The Design Of Fuzzy Gain Scheduling Of the Pd Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Han

2019

Electronics

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“…Perez has introduced a PID control law that depends on neural networks. Also, fuzzy PID controllers have been used in trajectory-tracking robotic systems [2]. Recently, evolutionary algorithms (EAs) have appeared as an alternative design methodology for robotic system applications [3] and [4].…”

Section: Introductionmentioning

confidence: 99%

Novel PID Tracking Controller for 2DOF Robotic Manipulator System Based on Artificial Bee Colony Algorithm

Elkhateeb

Badr

2017

Electrical, Control and Communication Engineering

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-This study presents a well-developed optimization methodology based on the dynamic inertia weight Artificial Bee Colony algorithm (ABC) to design an optimal PID controller for a robotic arm manipulator. The dynamical analysis of robotic arm manipulators investigates a coupling relation between the joint torques applied by the actuators and the position and acceleration of the robot arm. An optimal PID control law is obtained from the proposed (ABC) algorithm and applied to the robotic system. The designed controller optimizes the trajectory of the robot's end effector for a time-variant input and makes the robot robust in the presence of external disturbance.

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Self-Tuning Fuzzy Task Space Controller for Puma 560 Robot

Azarfar

Vahedi

2020

J. Electr. Eng. Technol.

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Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller

Cited by 21 publications

References 6 publications

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Tracking Control of Moving Sound Source Using Fuzzy-Gain Scheduling of PD Control

Novel PID Tracking Controller for 2DOF Robotic Manipulator System Based on Artificial Bee Colony Algorithm

Self-Tuning Fuzzy Task Space Controller for Puma 560 Robot

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