Quantum Particle Swarm Optimisation Proportional–Derivative Control for Trajectory Tracking of a Car-like Mobile Robot
Bibliographic record
Abstract
The goal of this research is to formulate and compare two algorithms, classical particle swarm optimisation (PSO) and quantum PSO (QPSO), for optimising the motion of a car-like mobile robot. Both algorithms are evaluated on the basis of their reduction and stabilisation of the root mean square error (RMSE) between the robot’s desired and actual trajectories. An implementation of the robot’s dynamic motion is provided. The robot’s mass and inertia are considered. The robot’s settings and the viscosity of the surroundings present a few obstacles to following the specified path. For each algorithm, the proportional (Kp) and derivative (Kd) parameters of the controller are optimised, and the convergence speeds and stabilities of the controllers are compared. The results show that both algorithms perform comparably. However, the QPSO method converges faster and is more stable at optimal Kp and Kd values. The ramifications of this research extend beyond trajectory tracking. Enhanced optimisation approaches can lead to higher performance in a variety of robotic systems, including autonomous cars, drones, and automation systems, by employing advanced quantum algorithms, such as QPSO.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".