Fuzzy Robust Trajectory Tracking Control of WMRs

Keighobadi, Jafar; Mohamadi, Yaser

doi:10.1007/978-1-4614-1695-1_7

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Considering the parameters uncertainty, measurement noises and disturbance torques which affect a real spherical robot, (8) is completed in the following form, Keighobadi (2012).…”

Section: Control System Designmentioning

confidence: 99%

Robust Sliding Mode Trajectory Tracking Controller for a Nonholonomic Spherical Mobile Robot

Azizi

Keighobadi

2014

IFAC Proceedings Volumes

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“…Considering the parameters uncertainty, measurement noises and disturbance torques which affect a real spherical robot, (8) is completed in the following form, Keighobadi (2012).…”

Section: Control System Designmentioning

confidence: 99%

Robust Sliding Mode Trajectory Tracking Controller for a Nonholonomic Spherical Mobile Robot

Azizi

Keighobadi

2014

IFAC Proceedings Volumes

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“…where, m is the total mass of the vehicle; t I is the AGV's moment of inertia around the normal axis of X-Y plane crossing through the point C; and w I denotes the inertia moment of driving wheels. Applying (9) in (8) and using the kinematical constraints (2) through (5) gives:…”

Section:  mentioning

confidence: 99%

“…Furthermore, robust sliding mode control techniques could be used to accomplish perfect path tracking when there are considerable uncertainties in the mathematical model of systems like an AGV [4,8]. Besides the complicated structure of sliding mode controllers integrated with the plant dynamics, this method is suffered by the chattering phenomenon [9].…”

Section: Introductionmentioning

confidence: 99%

Self-Constructing Neural Network Modeling and Control of an AGV

Keighobadi¹,

Fazeli²,

Shahidi³

2013

POS

Self Cite

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Tracking precision of pre-planned trajectories is essential for an auto-guided vehicle (AGV). The purpose of this paper is to design a self-constructing wavelet neural network (SCWNN) method for dynamical modeling and control of a 2-DOF AGV. In control systems of AGVs, kinematical models have been preferred in recent research documents. However, in this paper, to enhance the trajectory tracking performance through including the AGV’s inertial effects in the control system, a learned dynamical model is replaced to the kinematical kind. As the base of a control system, the mathematical models are not preferred due to modeling uncertainties and exogenous inputs. Therefore, adaptive dynamic and control models of AGV are proposed using a four-layer SCWNN system comprising of the input, wavelet, product, and output layers. By use of the SCWNN, a robust controller against uncertainties is developed, which yields the perfect convergence of AGV to reference trajectories. Owing to the adaptive structure, the number of nodes in the layers is adjusted in online and thus the computational burden of the neural network methods is decreased. Using software simulations, the tracking performance of the proposed control system is assessed.

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Terminal Sliding Mode Control for Quadrotors with Chattering Reduction and Disturbances Estimator: Theory and Application

Hou

2022

J Intell Robot Syst

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Fuzzy Robust Trajectory Tracking Control of WMRs

Cited by 4 publications

References 8 publications

Robust Sliding Mode Trajectory Tracking Controller for a Nonholonomic Spherical Mobile Robot

Robust Sliding Mode Trajectory Tracking Controller for a Nonholonomic Spherical Mobile Robot

Self-Constructing Neural Network Modeling and Control of an AGV

Terminal Sliding Mode Control for Quadrotors with Chattering Reduction and Disturbances Estimator: Theory and Application

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