Design and Implementation of Adaptive Backstepping Control for Position Control of Propeller-Driven Pendulum System
Bibliographic record
Abstract
The performance of the classical and adaptive backstepping control schemes for the angular position control of a nonlinear Propeller-Driven Pendulum System (PDPS) is investigated in this paper.A Particle Swarm Optimization (PSO) algorithm has been utilized to tune the design parameters of the proposed controllers.Based on the Lyapunov stability analysis the classical and the Adaptive Back-Stepping Controllers have been constructed in order to prove the convergence of the system's error with time.The Adaptive Backstepping Controller (ABSC) is designed to compensate for the variation in the system's mass magnitude.In terms of system transient response, a comparison study of the effectiveness of both controllers has been presented in this work.The simulation results have been obtained based on the MATLAB software.In addition, a comparison study between the proposed controllers and other controllers has been listed to demonstrate the effectiveness of the proposed controller.The simulation results show that the PSO based classical Backstepping Controller (BSC) has a better performance in terms of reducing the settling time, the steady-state error, and the Root Mean Square Error (𝑅𝑀𝑆𝐸) value in comparison with the STSMC and SMC.In addition, the simulation results reveal that the PSO based ABSC has a better performance in terms of reducing the steady state error and the maximum overshoot in comparison with the PSO based BSC and ASTSMC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".