System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques
Bibliographic record
Abstract
Developing a control solution for a quarter-car active suspension system, specifically aimed at enhancing ride comfort for individuals with spinal cord injuries while ensuring vehicle stability is important. Road irregularities are treated as external disturbances to the system. Traditional PID controllers often fall short due to issues like nonlinear dynamics, uncertain parameters, and limited robustness. To overcome these limitations, the hybrid optimization framework is used for controller tuning. A dataset comprising 397 car models is analyzed, and system parameters are selected using a combined Sequential Quadratic Programming and Pattern Search method. After validating the resulting dynamic model, various PID controllers are designed using standard metaheuristic algorithms-Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). Furthermore, two hybrid optimization strategies-Ant Colony Optimization with Genetic Algorithm (ACO-GA) and FminSearch with Simulated Annealing (Fmin-SA)-are applied to improve the control system's robustness and response. Among the performance metrics considered including Integral Square Error (ISE), Integral of Absolute Error (IAE), and Integral of Time Absolute Error (ITAE), the ISE criterion was found to consistently yield superior results and was therefore adopted for the controller design. Simulation results show that the ACO-GA-based PID controller achieves faster response compared to that of other approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".