Adaptive sliding-mode control of nonholonomic wheeled mobile robot

Koubaa, Yassine; Boukattaya, Mohamed; Damak, Tarak

doi:10.1109/sta.2014.7086759

Cited by 16 publications

(22 citation statements)

References 19 publications

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“…The results were unrealistic for a WMR with elevated loads and speeds [7]. This imposes significant limitations to the applicability of purely kinematic system [8].…”

Section: Introductionmentioning

confidence: 93%

Platooning strategy of mobile robot: simulation and experiment

et al. 2016

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Abstract. Concurrent studies show vehicle platooning system as a promising approach for a new transportation system. The platooning strategy can be also applied to automated mobile robots. Including dynamic modelling in the simulation with kinematic model would yield a different result as the dynamic modelling would include the physical parameters of the mobile robot. The aim is to create a model that describes the motion of a robot that follows another robot based on predetermined distance. Dynamic model of the proposed mobile robot is simulated and the kinematic modelling was included in to simulate the motion of the mobile robot. PID controller will be used as a controller for robot's motion and platooning strategy. A reference distance is given as the input and the PID controller computes the error and sends input to the mobile robot in the form of voltage. The robot is able to follow the leader robot by maintaining a distance of one metre with a small deviation in the direction as the robot tends to move towards the left due to forces acting on the wheel. This method can be implemented in a human following mobile robot where the leader robot is replaced with a human user.

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“…The results were unrealistic for a WMR with elevated loads and speeds [7]. This imposes significant limitations to the applicability of purely kinematic system [8].…”

Section: Introductionmentioning

confidence: 93%

Platooning strategy of mobile robot: simulation and experiment

et al. 2016

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“…Interaction forces on the x-axis and y-axis can be expressed in terms of Equations (35) and (36), as…”

Section: Interaction Modelmentioning

confidence: 99%

“…The classical dynamic equation for mobile robots can be expressed, according to [ 35 ], as

where, M is the symmetric inertial matrix, V is the centripetal and Coriolis matrix, E is the interaction matrix between surface and robot including friction effects, G is the gravitational vector,

is the robot’s spatial orientation vector defined as

is the vector of bounded unknown disturbances including unmodeled dynamics, B is the input matrix,

is the input vector, A is the matrix associated with kinematic constraints,

is the vector of Lagrange multipliers, q is the generalized coordinates defined as q =

.…”

Section: Dynamic Analysismentioning

confidence: 99%

Model Predictive Torque Control for Velocity Tracking of a Four-Wheeled Climbing Robot

Santos

Teixeira

Dalmedico

et al. 2020

Sensors

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Climbing robots are characterized by a secure surface coupling that is designed to prevent falling. The robot coupling ability is assured by an adhesion method leading to nonlinear dynamic models with time-varying parameters that affect the robot’s mobility. Additionally, the wheel friction and the force of gravity force are also relevant issues that can compromise the climbing ability if they are not well modeled. This work presents a model-based torque controller for velocity tracking in a four-wheeled climbing robot specially designed to inspect storage tanks. The model-based controller (MPC) compensates for the effects of nonlinearities due to the forces of gravity, friction, and adhesion through the dynamic and kinematic modeling of the climbing robot. Dynamic modeling is based on the Lagrange-Euler approach, which allows a better understanding of how forces and torques affect the robot’s movement. Besides, an analysis of the interaction force between the robot and the contact surface is proposed, since this force affects the motion of the climbing robot according to spatial orientation. Finally, simulations are carried out to examine the robot’s dynamics during the climbing movement, and the MPC is validated through the redrobot simulator V-REP and practical experiments. The presented results highlight the compensation of the nonlinear effects due to the robot’s climbing motion by the proposed MPC controller.

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“…The control law of the right wheel torque and left wheel torque for the mobile robot platform depends on the x-y position errors and the orientation error as in Eqs. (11) and (12), respectively.…”

Section: ) (K Hmentioning

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%

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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Adaptive sliding-mode control of nonholonomic wheeled mobile robot

Cited by 16 publications

References 19 publications

Platooning strategy of mobile robot: simulation and experiment

Platooning strategy of mobile robot: simulation and experiment

Model Predictive Torque Control for Velocity Tracking of a Four-Wheeled Climbing Robot

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

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