Fuzzy-Backstepping Controller Based on Optimization Method for Trajectory Tracking of Wheeled Mobile Robot

Swadi, Salah M.; Tawfik, Mauwafak Ali; Abdulwahab, Emad N.; Kadhim, Hasan Almgotir

doi:10.1109/uksim.2016.52

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…The dynamics model of the mobile robot is represented based on the Euler Lagrange formulation, as follows [10]:…”

Section: Fig 1 the Mobile Robot Platform Modelmentioning

confidence: 99%

“…These control algorithms have been proposed to solve the mobile robot motion control to follow the desired path with high performance of the controller in terms of generating an optimal control DOI: https://doi.org/10.33103/uot.ijccce.18.2.1 action that leads to minimizing the tracking pose error during tracking the desired path. These algorithms include the nonlinear neural PID controller [6], fuzzy logic and PID controllers [7], neural networks controllers [8 and 9], adaptive fuzzy with back-stepping controllers [10], adaptive sliding mode controllers [11], neural predictive controllers [12], [13], and nonlinear fractional order PID neural controllers [14], [15]. The motivation for this research is to find the best path with obstacles avoidance and to track and stabilize the mobile robot on the desired path by generating the best action without undesirable states such as a saturation state and a spike action state.…”

Section: Introductionmentioning

confidence: 99%

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A Cognitive System Design for Mobile Robot Based on an Intelligent Algorithm

2018

IJCCCE

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This paper presents a cognitive system based on a nonlinear Multi-Input Multi- Output (MIMO) Proportion Integral Derivative (PID) Modified Elman Neural Network (MENN) controller and the Square Road Map (SRM) method to guide the mobile robot during the continuous path-tracking with collision-free navigation through static obstacles. The proposed cognitive system consists of two parts: the first part is to plan the desired path for the mobile robot with the static obstacle environment in order to determine the target point and to avoid the obstacles based on the proposed square road map algorithm. The second part is to guide and track the wheeled mobile robot on the desired path equation based on the proposed nonlinear MIMO-PID-MENN controller with the intelligent algorithm. The Particle Swarm Optimization (PSO) is used to on-line tune the variable control parameters of the proposed controller to get the optimal torques actions for the mobile robot platform. Based on using the MATLAB package (2017), the numerical simulation results show that the proposed cognitive system has high accuracy for planning the desired path equation in terms of avoiding the static obstacles with smooth and short distance and generating a perfect torque action of (0.7 N.m) without a saturation state of (3.07 N.m), which leads to minimize the tracking pose error for the mobile robot to the zero value approximation. These results were confirmed by a comparative study with different nonlinear PID controller types in terms of number of iterations and the performance index.

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“…The dynamics model of the mobile robot is represented based on the Euler Lagrange formulation, as follows [10]:…”

Section: Fig 1 the Mobile Robot Platform Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Cognitive System Design for Mobile Robot Based on an Intelligent Algorithm

2018

IJCCCE

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“…The nonholonomic mobile robot with n generalized coordinates and subject to m constraints can be described by following Equation (Swadi et al, 2016;Ali et al, 2014;Tawfik et al, 2014;Dhaouadi and Hatab., 2013;Mohareri, 2009;Sidek, 2008):…”

Section: Dynamics Of the Differential Drive Mobile Robotmentioning

confidence: 99%

Differential Drive Mobile Robot Trajectory Tracking With Using Pid and Kinematic Based Backstepping Controller

Demirbaş¹,

Kalyoncu²

2017

Scitech

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ABSTRACT:In this study, the mathematical model of a nonholonomic vehicle was derived. A PID and kinematic based backstepping controller (KBBC) was designed for a differential drive mobile robot to be able to track a desired trajectory. The KBBC was used to overcome the nonlinearity of the trajectory tracking and the PID controllers was used for the DC motors' speeds adjustments. Responses of the vehicle's controller in the square shaped trajectory had obtained and results were graphically presented. The effectiveness of the designed controller has been discussed.

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“…For example, the Segway self-balancing vehicle developed in the United States uses multiple sensors and high-speed microcontrollers to detect the vehicle's attitude and adjust in real time by detecting changes in the driver's center of gravity 3 . The NXTway self-balancing scooter designed and manufactured in Japan adds controllers that can control functions like moving forward, backward and playing music 4 . In general, the characteristic of an intelligent self-balancing two-wheeled scooter is that it is not easy to maintain balance when stopped, but it can keep balanced with front and back movement through algorithms during motion.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of two-wheeled self-balancing intelligent scooter

Liu,

Hou,

Huang

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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With the development of the times, intelligence has become more and more the theme of the era, and intelligent mobile devices are more applicable to various production and living in society. In order to reduce costs, increase applicability, and improve performance, this project designs a two-wheeled self-balancing scootr simulation. The scooter uses the STM32F103 chip as the main controller, uses the MPU-6050 to collect the tilt angle and motion speed of the vehicle body, and uses the PID algorithm to control the state of the vehicle body. The PID algorithm uses the given speed and calculates the acceleration through proportional, integral and differential three variables; then it controls the motion of the DC motor through the driver chip. The DC motor feeds back the speed to the main chip through the encoder to achieve self-balancing of the two-wheeled scooter when powered on. This design can also connect to a mobile APP through a Bluetooth module to control the movement of the scooter.

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Fuzzy-Backstepping Controller Based on Optimization Method for Trajectory Tracking of Wheeled Mobile Robot

Cited by 9 publications

References 9 publications

A Cognitive System Design for Mobile Robot Based on an Intelligent Algorithm

A Cognitive System Design for Mobile Robot Based on an Intelligent Algorithm

Differential Drive Mobile Robot Trajectory Tracking With Using Pid and Kinematic Based Backstepping Controller

Design and Implementation of two-wheeled self-balancing intelligent scooter

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