A chattering-free fuzzy hybrid sliding mode control of an electrohydraulic active suspension
Bibliographic record
Abstract
This paper introduces a new hybrid controller for position and force control of an electrohydraulic car active suspension. In most hybrid controllers, a switching function is normally used in order to take advantage of two separate controllers. Switching between the two controllers produces a chattering in the system, in addition to the chattering that may be inherent to the controller itself, which deteriorates the system performance. In this work, we resolved the switching limitations dilemma by transitioning from one controller to another through two low-pass filters. These filters are used with variable gains to improve the new hybrid position/force controller performance that we developed. The produced control signal is a structured combination, in which the signal coming from the position controller reduces the effect of road perturbations on passengers by bringing the car’s vertical motion to zero. Simultaneously, the signal from the force controller tracks a reference force and thus reduces the force transmitted to passengers. To eliminate the chattering that is inherent to the sliding mode controller, we introduced an exponential reaching law function to the hybrid sliding mode controller. This exponential function also reduced the response time, consequently speeding up the system reaction to suppress perturbations. In addition to that, a recent sliding surface-based controller is applied to vary the filters’ gains and obtain better performance. The frequency analysis is done to verify the controller performance. The proposed hybrid controller is also validated in real time on active suspension workbench and compared with a classical PID controller.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".