A Nonlinear Integral Sliding Surface to Improve the Transient Response of a Force-Controlled Pneumatic Actuator With Long Transmission Lines
Bibliographic record
Abstract
Abstract A force-controlled pneumatic actuator with long connecting tubes is a well-accepted solution to develop magnetic resonance imaging (MRI)-compatible force control applications. Such an actuator represents an uncertain, second-order, nonlinear system with input delay. The integral sliding mode control, because of guaranteed robustness against matched uncertainties throughout the system response, provides a favorable option to design a robust controller for the actuator. However, if the controller is based on a linear integral sliding surface (LISS), the response of the actuator overshoots, especially when there are large initial errors. Minimizing overshoot results in a smaller controller bandwidth and a slower system response. This paper presents a novel nonlinear integral sliding surface (NLISS) for a sliding mode controller to improve the transient response of the actuator. The proposed surface is a LISS augmented by a nonlinear function of tracking error and does not have a reaching phase when there are initial errors and even multiple steps in the desired trajectory. The surface enables the integral sliding mode controller to offer variable damping, which changes from low to high as the transient error approaches small values and vice versa. Simulation studies and experimental results show that the controller based on the proposed sliding surface successfully eliminates the overshoot without compromising the controller bandwidth, rise, and settling times. For performance evaluation, the controller parameters are tuned using the globalized and bounded Nelder–Mead (GBNM) algorithm with deterministic restarts. The study also establishes the asymptotic stability of the controller based on the proposed sliding surface using Lyapunov's stability criterion.
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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 | 0.001 | 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".