Dynamic identification and model based control of an omni-wheeled mobile robot

Abhishek, Vishal; Saha, Subir Kumar

doi:10.1109/icrom.2016.7886810

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…An input-output linearization is valid under a solid assumption of the model's accuracy if the dynamical model is experimentally validated, as in [30]. However, canceling the nonlinearities can be imprecise due to the unmodeled perturbations or disturbances, which is to be expected, especially in practical implementations.…”

Section: Introductionmentioning

confidence: 99%

Leader–Follower Formation and Disturbance Rejection Control for Omnidirectional Mobile Robots

Ramírez-Neria,

González-Sierra,

Madonski

et al. 2023

Robotics

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This paper proposes a distance-based formation control strategy with real-time disturbance rejection for omnidirectional mobile robots. The introduced control algorithm is designed such that the leader tracks a desired trajectory while the follower keeps a desired distance and formation angle concerning the leader. In the first step, the evolution of distance and formation angle is obtained from a perturbed second-order dynamic model of the robot, aided by a general proportional integral observer (GPIO), added to estimate unwanted disturbances. Then, the control law is designed for both robots via the active disturbance rejection control (ADRC) methodology, which only depends on the position, distance, and orientation measurements. A numerical simulation compared with a robust controller exhibits the system’s behavior. Furthermore, a set of laboratory experiments is conducted to verify the performance of the proposed control system, where a motion capture system is used as a proof of concept. In this context, this is considered a previous step for further experimentation with onboard sensors.

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Section: Introductionmentioning

confidence: 99%

Leader–Follower Formation and Disturbance Rejection Control for Omnidirectional Mobile Robots

Ramírez-Neria,

González-Sierra,

Madonski

et al. 2023

Robotics

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show abstract

“…In the most commonly used types of wheel robots with two [4,5] with four [6,7] and more wheels the static and dynamic parameters are monitored, while using the kinematic chassis structures as a basis. Dynamic and kinematic principles are used to create mobile robot models [8,9]. In the control of non-linear systems, control methods with fuzzy logic are used quite often.…”

Section: Introductionmentioning

confidence: 99%

Identification of Dynamics of Movement of the Differential Mobile Robotic Platform Controlled by Fuzzy Controller

et al. 2019

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Mobile robots with differential chassis are very often used because of simple construction and a smaller number of drive and sensors elements. For practical applications, it is necessary to know the kinematic and dynamic structure of the differential mobile robot. This paper deals with identification of the dynamics of the differential robotic platform, using differential kinematics. Electro-optical rpm sensors obtain required values such as speed of the driven wheels. Identification of dynamic system is used to determine the dynamic characteristics of power subsystem of developed EN 20 robot, whose control subsystem is created by single-chip microcontroller. Response of the dynamic system is monitored along with the peripheral velocity of the right and left drive wheels. Incremental encoders that work on optics principle measure the speeds of both wheels. It was necessary to calibrate the sensors and obtain constants for precise speed determination. The monitored system with the dumped oscillation characteristic is approximated by a system with the inertia of the 2 nd order. Dynamic system parameters are found. The system approximation is suitable for given evolution of circumferential speeds of the right and left wheels. This is confirmed by the quantitative determination coefficients R 2. The equations for calculating peripheral velocities of driving wheels are applied to the system of the differential equations for the differential chassis. A mathematical model of the mobile robot EN20 was obtained for testing control algorithms, where a robot is equipped with sensory systems and it is designed for interior conditions. Fuzzy controller with 49 interference rules is used to control the mobile robot. The real mobile robot path matches the path determined according to simulation model.

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A novel inertia moment estimation algorithm collaborated with Active Force Control scheme for wheeled mobile robot control in constrained environments

Ali

Radzak

Mailah

et al. 2021

Expert Systems with Applications

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Dynamic identification and model based control of an omni-wheeled mobile robot

Cited by 5 publications

References 4 publications

Leader–Follower Formation and Disturbance Rejection Control for Omnidirectional Mobile Robots

Leader–Follower Formation and Disturbance Rejection Control for Omnidirectional Mobile Robots

Identification of Dynamics of Movement of the Differential Mobile Robotic Platform Controlled by Fuzzy Controller

A novel inertia moment estimation algorithm collaborated with Active Force Control scheme for wheeled mobile robot control in constrained environments

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