Whole-Body Control of an Autonomous Mobile Manipulator Using Model Predictive Control and Adaptive Fuzzy Technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Whole-body control (WBC) has emerged as an important framework in manipulation for mobile manipulators. However, most existing WBC frameworks require known dynamics. Considering whole-body manipulation and optimization with unknown dynamics, this article presents the WBC of a nonholonomic mobile manipulator using model predictive control (MPC) and fuzzy logic system. First, by constructing a dynamics-based feedback linearized robotic multi-input-multi-output (MIMO) system, an MPC-based WBC strategy is proposed for mobile manipulator. Such a strategy can provide the optimal control inputs with the specified optimization index and constraints. Thereafter, a primal-dual neural network effectively addresses the constrained quadratic programming (QP) problem over a finite receding horizon brought by the MPC. Then, in order to convert the intermediate control signals into the optimal control torques that can be executed by actuators, an adaptive FLS is employed to approximate the unknown dynamics. The novel elements of the current design control approach refer to the dynamics-based feedback linearized robotic MIMO system and the combination of an MPC module with an adaptive fuzzy controller. Finally, the trajectory tracking experiments performed on a mobile dual-arm robot demonstrate the effectiveness of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it