Nonlinear Model Predictive Control of Rover Robotics System
Bibliographic record
Abstract
The paper presents two robust and efficient control algorithms based on (i) Optimal Control Allocation (OCA) and (ii) Nonlinear Model Predictive Control (NMPC).The robotics system consists of two rovers with mecanum wheels and mounted two 7-DOF arms carrying a common load.The overall system is an underdetermined one with non-holonomic constraints.The developed control algorithms focus on providing an optimal solution to the wheel and joint torque saturation problem, which is typically encountered while manipulating a large and heavy payload.The first control algorithm based on OCA minimizes a quadratic cost function consisting of robot joint and rover wheel torques, contact forces, and moments using only the current state values and the system dynamics.It is computationally very efficient.The NMPC algorithm minimizes a quadratic cost function which not only includes the current states but also the future state estimates, and the control inputs over a specified prediction horizon.The system consisting of multi-rover with a dual arm is highly non-linear.The linear MPC technique on which most of the previous studies relied is not adequate.On the other hand, the computational difficulties of a generic NMPC algorithm is remarkably high.In this paper, an elegant, discretized technique with exact realization is implemented to take into account the full non-linear model and yet provide a simple real-time solution satisfying a minimum performance index subject to constraints.The results show that the developed control algorithms OCA and NMPC work efficiently, and the minimum the contact moments and forces, and the joint torques are realized while two arms carry a common load and successfully track a reference end-effector trajectory.The results also indicate that although NMPC algorithm is computationally more involved, it provides superior results in reducing joint and wheel torques as well as contact moments and forces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".