Trajectory Tracking Control Employing Nonlinear Compensator and State Observer for Photothermal-Driven Liquid Crystal Elastomer Actuator
Bibliographic record
Abstract
The trajectory tracking control for the photothermal-driven liquid crystal elastomer (LCE) actuator presents a significant challenge due to its hysteresis nonlinear characteristic and its inherent complex deformation mechanism. To address this challenge, this article proposes a trajectory tracking control method for the LCE actuator utilizing a nonlinear compensator and a state observer. The proposed control is a multistep control, which includes temperature control from the input voltage to the LCE temperature and displacement control from the LCE temperature to the LCE displacement. In the proposed method, we design a non-Lipschitz continuous state-feedback controller to realize finite-time convergence control of the temperature. As for the displacement control, we design a state observer to estimate the change rate of the LCE displacement. Meanwhile, a nonlinear inverse compensator is designed to compensate for the hysteresis nonlinearity of the LCE dynamics, which simplifies the complex nonlinear control problem into a linear control problem. Hence, the pole placement method can be utilized to design a trajectory tracking controller to achieve the control objective. The proposed control method is validated by tracking control experiments with different target trajectories.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".