Sliding Mode Controller and Filter Applied to a Pneumatic McKibben Muscle Actuator
Bibliographic record
Abstract
In this paper, a robust and stable control strategy is applied to a Festo fluidic muscle actuator, with the objective of trajectory following control. A complete model of this system is not available which leads to unmodeled dynamics and uncertainties. Furthermore, full-state feedback is required for this type of control. However, in practice not all of the states are measurable or available due to cost or availability of instruments, thus a full-state observer is required. The Smooth Variable Structure Filter (SVSF) is a recently introduced robust predictor-corrector method used for state and parameter estimation, and has a form that is able to provide full-state information. In this regard, a new strategy that combines Sliding Mode Control (SMC) with the SVSF is used to control this system. The estimated states from the SVSF are used by the sliding mode controller to obtain a discontinuous control signal. This signal drives the plant to follow a desired state trajectory required by the pneumatic McKibben muscle actuator. Simulation results were generated based on a realistic desired trajectory. The results of the SMC-SVSF control strategy are compared with a tuned PID controller. The described control strategy is able to overcome the nonlinearities present in the system, has a fast response time, and is robust to modeling uncertainties and measurement noise.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".