Posture Stabilization of Wheeled Mobile Robot Based on Passivity-Based Robust Switching Control with Model Uncertainty Compensation

This study presents apassivity-based robust switching control for the posture stabilization of wheeled mobile robots (WMRs) with model uncertainty. Essentially, this proposed strategy is switching between (1) passivity-based robust control to lead the robot to the neighborhood of local minima with a...

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Bibliographic Details
Published in:Applied sciences 2019-12, Vol.9 (23), p.5233
Main Authors: Jung, DaeYi, Bang, Suji
Format: Article
Language:English
Subjects:
Compensation
Control theory
Convergence
Energy
Kinetic energy
Partial differential equations
Passivity
Posture
Potential energy
Robot control
Robots
Robust control
Stabilization
Uncertainty
Velocity
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Summary:This study presents apassivity-based robust switching control for the posture stabilization of wheeled mobile robots (WMRs) with model uncertainty. Essentially, this proposed strategy is switching between (1) passivity-based robust control to lead the robot to the neighborhood of local minima with a finite time and (2) another robust control to perturb the w-rotational motion of the WMR before the v-kinetic energy of the WMR become meaningless, thereby, eventually converging to the desired posture. Thus, combining two switching control laws ensures the global convergence of (x,y)-navigation of WMRs from any initial position to desired set. Especially, the inter-switching time is intentionally selected before the WMR completely loses its mobility, which ensures a strict decrease in (x,y)-navigation potential energy and a better global convergence rate. In addition, this control architecture also includes model uncertainty compensation, often neglected in practice, and analytical study of rotational perturbation was also conducted. The Lyapunov technique and energetic passivity were utilized to derive this control law. Simulation results are presented to illustrate the effectiveness of the proposed technique. It was found from the results that the WMR was quickly converged to the desired posture even under the presence of model uncertainty.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9235233