Trajectory Tracking Control of Wheeled Mobile Manipulators With Joint Flexibility via Virtual Decomposition Approach
Bibliographic record
Abstract
Wheeled mobile manipulators (WMMs) involving a wheeled mobile platform and a serial manipulator are finding increasing applications in diverse fields, creating new challenges in performing high-precision operations in a spacious workspace. WMMs are challenging to control due to uncertainties in system parameters, coupled dynamics, and external disturbances, which make stability guarantees difficult. This paper proposes a virtual decomposition control (VDC)-based trajectory tracking controller for WMMs, addressing joint flexibility, external disturbances, etc. The proposed method uses a VDC-based iterative approach to manage the complex coupled dynamics and employs a separate adaptive controller to handle joint flexibility. The robotic system’s stability is validated using the specific features of VDC (proof of each subsystem’s virtual stability) according to the Lyapunov stability theory. The advantages and effectiveness of the proposed method are demonstrated through experiments.Note to Practitioners—This paper addresses the challenges faced in controlling WMMs, which are becoming increasingly common in various industrial and service applications due to their ability to perform tasks in large and dynamic environments. The coupling between the wheeled platform and the manipulator, as well as uncertainties in system parameters such as joint flexibility and external disturbances, make precise trajectory tracking difficult. To address these challenges, this paper presents a control approach based on VDC, which breaks down the complex system into manageable subsystems and ensures stability for each part individually. The control strategy also incorporates adaptive control to handle joint flexibility and unpredictable disturbances. The stability of the system is rigorously proven through Lyapunov theory, ensuring robust performance under real-world conditions. Practitioners working on autonomous mobile robots equipped with manipulators may find this approach useful for improving trajectory tracking performance in uncertain and dynamic environments. However, the practical implementation of this method will require careful tuning of controller parameters and real-time computational capabilities to ensure seamless operation in real applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".