System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty

Zhong, Guoliang; Kobayashi, Yukinori; Hoshino, Yohei; Emaru, Takanori

doi:10.1007/s11071-013-0776-0

Cited by 35 publications

(24 citation statements)

References 26 publications

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“…In [3], adaptive control strategies have been proposed for tracking of a robot. This method uses a combination of adaptive fuzzy control and back-stepping.…”

mentioning

confidence: 99%

“…This adaptive updating is called the fuzzy learning process. The methods used in our paper can also be incorporated in [3] by considering the dynamics with fuzzy controller in the presence of white Gaussian noise. Specifically in [3], the authors construct a Lyapunov function which decreases with time provided that the fuzzy approximation error to F(q, q) is small.…”

mentioning

confidence: 99%

“…The methods used in our paper can also be incorporated in [3] by considering the dynamics with fuzzy controller in the presence of white Gaussian noise. Specifically in [3], the authors construct a Lyapunov function which decreases with time provided that the fuzzy approximation error to F(q, q) is small. It would be interesting to see how the ensemble averages Lyapunov function of [3] behaves in the presence of white Gaussian noise in the dynamics.…”

mentioning

confidence: 99%

“…Specifically in [3], the authors construct a Lyapunov function which decreases with time provided that the fuzzy approximation error to F(q, q) is small. It would be interesting to see how the ensemble averages Lyapunov function of [3] behaves in the presence of white Gaussian noise in the dynamics. The techniques used in our paper can be applied to [3].…”

mentioning

confidence: 99%

“…It would be interesting to see how the ensemble averages Lyapunov function of [3] behaves in the presence of white Gaussian noise in the dynamics. The techniques used in our paper can be applied to [3].…”

mentioning

confidence: 99%

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Statistical analysis of tracking and parametric estimation errors in a 2-link robot based on Lyapunov function

2015

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A convergence analysis of the angular position and velocity tracking errors and parametric estimation errors for a 2-link robot dynamics in the absence of noise exist in the literature (Slotine and Li in Applied nonlinear control. Prentice-Hall Inc, Englewood Cliffs, NJ, 1991). In this paper, we consider the effect of noise on the robot dynamics as well as on the parameter update equation. Noise in the robot dynamics arises due to errors in reading q,q, i.e., the angular positions and velocities of the robot link that are used for calculating the errors e,ė used to generate the feedback torque via the PD controller. Noise is chosen as Gaussian because it is produced as the cumulative effect of several small independent random effects like electrons moving in the motor wire and dust friction, and we know from the central limit theorem that the sum of a large number of small independent effects has a Gaussian distribution. Noise in the parametric update equation arises due to finite register effects in the digital computer. Both of these noises are modeled as white Gaussian, and the robot and parametric update dynamics have thus been modeled as nonlinear coupled stochastic differential equations. Using linearization of this coupled SDE system around the non-random components of the trajectory estimation error and parametric estimation errors, we first derive stochastic integral expressions for the random components in these errors. We then evaluate the average rate of change of the Lyapunov energy function using Ito calculus and obtain conditions on the noise amplitude so that the limiting value of this average rate of change is negative, guaranteeing asymptotic average stability of the system and parametric estimation algorithm. One of the novelties of this work is the new algorithm for parameter estimation which enables us to improve on the algorithm convergence rate as compared to previous work (Slotine and Li 1991). Finally, our stochastic algorithm is verified by performing hardware experiments with the actual PHANToM Omni robot.

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“…In [3], adaptive control strategies have been proposed for tracking of a robot. This method uses a combination of adaptive fuzzy control and back-stepping.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

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Statistical analysis of tracking and parametric estimation errors in a 2-link robot based on Lyapunov function

2015

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Finite‐time control of mobile manipulators subject to unknown/unstructured external disturbances

Galicki

2022

Intl J Robust & Nonlinear

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A robust finite-time control law is proposed for mobile manipulators with uncertain both kinematic and dynamic equations. Moreover, the following external disturbances are allowed to act on the system: unstructured forces exerted on the end-effector, slip reaction forces affecting the platform wheels, unknown friction forces coming from joints directly driven by the actuators and undesirable unstructured forces caused by singular configurations. In order to cope with both parametric uncertainties and unknown/unstructured external disturbances, we first transform the original control problem whose non-holonomic constraints are violated into a steering one in an extended space of generalized coordinates with extended non-holonomic constraints of the Pfaffian form and then introduce a task space non-singular terminal sliding manifold. Based on the Lyapunov stability theory, a new class of estimated extended transposed Jacobian control laws is derived which both effectively counteract the external forces and significantly reduce the slipping phenomena. A second order sliding technique is involved in our control law to eliminate undesirable chattering effect. An illustrative example of a mobile manipulator control demonstrates the advantages of the approach. Moreover, numerical comparisons with other representative controllers well-known in the literature are also given.

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Cooperative Control of Sliding Mode for Mobile Manipulators

et al. 2018

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System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty

Cited by 35 publications

References 26 publications

Statistical analysis of tracking and parametric estimation errors in a 2-link robot based on Lyapunov function

Statistical analysis of tracking and parametric estimation errors in a 2-link robot based on Lyapunov function

Finite‐time control of mobile manipulators subject to unknown/unstructured external disturbances

Cooperative Control of Sliding Mode for Mobile Manipulators

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