Nonlinear Autonomous Control of a Two-Wheeled Inverted Pendulum Mobile Robot Based on Sliding Mode

Sinha, Anupama; Prasoon, Pikesh; Bharadwaj, Prashant Kumar; Ranasinghe, Anuradha

doi:10.1109/cine.2015.20

Cited by 13 publications

(11 citation statements)

References 15 publications

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“…By partial differentiation of the equation 7for each of (,̇,̇) then, To linearize the nonlinear equations that describe the two wheeled inverted pendulum system, the Jacobian method is used as follows [12]:…”

Section: System Modelingmentioning

confidence: 99%

Full State Feedback 𝐇𝟐 and H-infinity Controllers Design for a Two Wheeled Inverted Pendulum System

Ali¹,

Shareef²

2018

ETJ

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In this work, two robust controllers, which are full state feedback, 2 and full state feedback ∞ controllers are proposed for the two wheeled inverted pendulum system. The nonlinear equations for the two wheeled inverted pendulum system are developed using Euler-Lagrange equation. The system parameters changes are considered to show the effectiveness of the proposed robust controllers. These controllers are proposed not only to stabilize the pendulum in upright position but also to drive the position to track a given reference input. The results show that more desirable robustness and time response specifications can be achieved using the proposed controllers. The effectiveness of the proposed controllers is verified experimentally using real two wheeled inverted pendulum system.

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Section: System Modelingmentioning

confidence: 99%

Full State Feedback 𝐇𝟐 and H-infinity Controllers Design for a Two Wheeled Inverted Pendulum System

Ali¹,

Shareef²

2018

ETJ

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“…Thus the error candidates can be given as (8) i can stand for both primary and secondary user. The errors require to be stabilized to the origin for accurate tracking, or the error variable should tend to small vicinity of zero after a transient of acceptable duration [7]. The origin is always a stable equilibrium and the coordinate axes can always be translated to suit the requirements whenever this is not the case.…”

Section: Case Of Best Effort Traffic Servicesmentioning

confidence: 99%

“…This control strategy eliminates the need for exact modeling and provides very good disturbance rejection. Using this control, the dynamic behavior of the system can be tailored using a particular choice of sliding function [7]. Under uncertainties such as link failures and in both steady state and non-stationary conditions, this controller delivers a high performance.…”

Section: Introductionmentioning

confidence: 99%

Robust nonlinear congestion controller for cognitive radio based wireless network

Majumder

Singh

Sahux

et al. 2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

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Under the constraints of limited bandwidth and exponentially rising user demand, there is a dire need to maximize throughput. Maintaining Quality of Service (QoS) in a communication network demands congestion control with high accuracy. This paper focuses on this challenging task that incorporates design of effective congestion controller to reduce packet loss in cognitive radio networks. Limited buffer capacity and bandwidth impose restriction on the performance when number of requests increase. The controller is designed on the notions of sliding mode which is a variable structure control technique known for its robustness and disturbance rejection capabilities. An optimal design strategy is used in development of the controller. The efficiency of the controller is confirmed by numerical simulations. Keywords-Quality of Service (QoS), Cognitive Radio Net works (CRN), opportunistic spectrum utilization, fluid flow model, non stationary Poisson process, quadratic minimization, infinite switching, sigmoid functions.

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“…Combination of a robust pole-placement design and a linear quadratic regulator [3], a linear quadratic regulator optimized by a particle swarm algorithm [4], a Fuzzy Immune PD controller and robust sliding mode controllers for accurate tracking and disturbance rejection [5,6]. Are some examples.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Control of Two-wheeled Mobile Robot

Jahromi¹,

Shishavan²

2019

ujeee

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In this paper, a study of the GBOT1001 two wheeled mobile robot is presented. Linearized dynamic equations are used to design a controller for the inherently unstable mobile robot. A Fuzzy controller is designed for the robot to direct the orientation of the chassis. Then linear quadratic regulator is also incorporated into the closed-loop system. The parameters of the LQR controller are computed first with the trial and error methods then, with the aid of the Genetic Algorithm. The resulting controller is referred to as the Fuzzy-TE-LQR and Fuzzy-GA-LQR controller respectively. The closed-loop responses for both controllers are obtained via computer simulations. Simulation results show that the Fuzzy-GA-LQR controller exhibits a less oscillatory closed-loop response that is achieved with a reasonable control effort.

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Nonlinear Autonomous Control of a Two-Wheeled Inverted Pendulum Mobile Robot Based on Sliding Mode

Cited by 13 publications

References 15 publications

Full State Feedback 𝐇𝟐 and H-infinity Controllers Design for a Two Wheeled Inverted Pendulum System

Full State Feedback 𝐇𝟐 and H-infinity Controllers Design for a Two Wheeled Inverted Pendulum System

Robust nonlinear congestion controller for cognitive radio based wireless network

A New Approach for Control of Two-wheeled Mobile Robot

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