Adaptive robust control of Mecanum-wheeled mobile robot with uncertainties

Alakshendra, Veer; Chiddarwar, Shital S.

doi:10.1007/s11071-016-3179-1

Cited by 119 publications

(65 citation statements)

References 28 publications

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“…The first approach, analysed in works [2][3][4][5][6][7][8][9][10][11][12][13], utilises the formulation of extended (augmented) task space (including also input-output linearisation techniques) in the inverse kinematics problem. It is based on extending the dimension of the task space by incorporating as many additional constraints as the degree of the redundancy.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, control algorithms from [2][3][4][5][6][7][8] require full knowledge of the dynamic equations. The controllers offered in [9][10][11][12][13][14] need the knowledge of desired trajectories for both the end effector and non-holonomic platform and are not optimal in any sense. Steering signals generated in works [10][11][12][13][14] are discontinuous and require the knowledge of the holonomic manipulator and platform velocities.…”

Section: Introductionmentioning

confidence: 99%

“…The controllers offered in [9][10][11][12][13][14] need the knowledge of desired trajectories for both the end effector and non-holonomic platform and are not optimal in any sense. Steering signals generated in works [10][11][12][13][14] are discontinuous and require the knowledge of the holonomic manipulator and platform velocities. Using both fuzzy logic system to compensate for modelling uncertainties and a robust term to ensure system stability, a trajectory tracking controller has been proposed in work [12], which, however, generates also discontinuous steering signals.…”

Section: Introductionmentioning

confidence: 99%

“…Using both fuzzy logic system to compensate for modelling uncertainties and a robust term to ensure system stability, a trajectory tracking controller has been proposed in work [12], which, however, generates also discontinuous steering signals. An adaptive robust trajectory tracking controller for a mecanumwheeled mobile robot has been offered in [13]. To make the system robust, the authors in [13] have designed a second-order sliding mode law providing also discontinuous steering signals.…”

Section: Introductionmentioning

confidence: 99%

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Optimal cascaded control of mobile manipulators

Galicki

2019

Nonlinear Dyn

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In this study, the solution to the kinematically optimal control problem of the mobile manipulators is proposed. Both dynamic equations are assumed to be uncertain, and globally unbounded disturbances are allowed to act on the mobile manipulator when tracking the trajectory by the end effector. We propose a computationally efficient class of cascaded control algorithms, which are based on an extended Jacobian transpose matrix. Our controllers involve two new nonsingular terminal sliding mode manifolds defined by nonlinear integral equalities of both the second order with respect to the task space tracking error and the first order with respect to reduced mobile manipulator acceleration. Using the Lyapunov stability theory, we prove that the proposed Jacobian transpose cascaded control schemes are finite time stable provided that some practically reasonable assumptions are fulfilled during the mobile manipulator movement. The numerical examples carried out for mobile manipulators [consisting of a non-holonomic platform of type (2, 0) and holonomic manipulators of 2 and 3 revolute kinematic pairs], which operate in two-dimensional and threedimensional work spaces, respectively, illustrate both the trajectory tracking performance of the proposed control schemes and simultaneously their minimising property for some practically useful objective function.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal cascaded control of mobile manipulators

Galicki

2019

Nonlinear Dyn

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“…1. Compared with the trajectory tracking control schemes of OMRs, [14][15][16]18 the state constraints for position, rotation angle, longitudinal velocity, transverse velocity, and rotating speed are considered in this article, which can guarantee the safety or meet requirements of the operating environment in practical applications. 2.…”

Section: Introductionmentioning

confidence: 99%

Dynamic surface control–based adaptive neural tracking for full-state constrained omnidirectional mobile robots

Zheng

Ito

2019

Advances in Mechanical Engineering

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This article studies the neural network-based adaptive dynamic surface control for trajectory tracking of full-state constrained omnidirectional mobile robots. The barrier Lyapunov function method is adopted to handle the full-state constraints of the omnidirectional mobile robot, and thus state variables will never violate the restrictions. Then, the neural network is used to approximate the uncertain system dynamics, and the adaptive law is proposed to adjust the weights. Moreover, the dynamic surface control is adopted to avoid the derivation of virtual variables, and the complexity of the controller can be simplified in comparison with the classical backstepping technique. The auxiliary system is proposed as the compensator to address the input saturation of omnidirectional mobile robots. All signals including tracking errors, state variables, adaptive parameters, and control inputs in the closed-loop system are proved to be uniformly bounded, while the control gains are chosen properly. Numerical simulations are tested to validate the effectiveness and advancements of the given control strategy.

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Event‐Triggered Adaptive Neural Network Control for 4WS4WD Wheeled Mobile Robot

Liao,

Liu,

et al. 2024

Adaptive Control & Signal

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In this article, an event‐triggered adaptive neural network controller based on threshold band is designed for a four wheels independently steered and four wheels independently driven (4WS4WD) mobile robot. The 4WS4WD mobile robot is attracting attention for its excellent motion performance such as manipulation versatility and posture flexibility. However, its control difficulty is increased due to its characteristics of being controlled by eight motors for steering and driving respectively. Also, since the robot itself has limited computing and communication resources, real‐time control cannot be guaranteed. To copy with that, the kinematic and dynamics models are first introduced for the 4WS4WD mobile robot. Second, an adaptive neural network controller with low‐frequency learning rate is utilized to control the mobile robot since there are unknown perturbations in the model. It can maintain system stability while handing unidentified model perturbations. The stability of the controller is demonstrated by Lyapunov stability analysis. An event‐triggered based on threshold band is suggested to lessen the amount of computation in the control process. Finally, the simulation outcomes further demonstrate how the suggested approach can greatly lessen the computational and communication cost while maintaining control performance.

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Adaptive robust control of Mecanum-wheeled mobile robot with uncertainties

Cited by 119 publications

References 28 publications

Optimal cascaded control of mobile manipulators

Optimal cascaded control of mobile manipulators

Dynamic surface control–based adaptive neural tracking for full-state constrained omnidirectional mobile robots

Event‐Triggered Adaptive Neural Network Control for 4WS4WD Wheeled Mobile Robot

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