Nonlinear Model Predictive Control for Trajectory Tracking of Omnidirectional Robot Using Resilient Propagation
Bibliographic record
Abstract
This paper proposes an enhanced Nonlinear Model Predictive Control (NMPC) framework that incorporates a robust, convergent variant of the resilient propagation (RPROP) algorithm to efficiently solve the Nonlinear Optimization Problem (NOP) in real time. The controller is developed for both constrained and unconstrained trajectory tracking of Wheeled Mobile Robots (WMRs), with operational constraints handled via the external penalty method. The proposed method introduces adaptive step sizes and a backtracking mechanism, significantly improving convergence speed without compromising accuracy. Simulation results show that, even under constraints, the proposed method reduces computational time by a factor of 6 to 11 compared to the Interior Point method and 2 to 4 compared to the Active Set method. In addition, it achieves superior tracking accuracy, with root mean square (RMS) position tracking errors reduced by approximately 50% relative to the benchmark methods. Real-time experiments conducted on the Robotino Festo Omnidirectional Mobile Robot (OMR) validate the method’s practical effectiveness, demonstrating faster convergence and improved velocity tracking performance, while maintaining comparable or better position tracking. These findings establish the proposed controller as a computationally efficient and accurate solution for real-time WMR trajectory tracking.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".