Output Feedback With Feedforward Robust Control for Motion Systems Driven by Nonlinear Position-Dependent Actuators
Bibliographic record
Abstract
This paper introduces a control approach for a motion system driven by a class of actuators with multiple nonlinearities. The proposed approach presents a combination of a feedforward controller and an output feedback controller to achieve a tracking performance of the motion system. The feedforward controller is mainly proposed to address the actuator dynamics and provide a linearization without requiring measurements from the actuator. Subsequently, the output feedback controller is designed using the measured position to achieve a tracking objective for a desired reference signal, considering the unknown nonlinearities in the system and the error due to the open-loop compensation using feedforward control. The efficacy of the proposed control approach is validated through three applications: reluctance actuator, electrostatic microactuator, and magnetic levitation system. Both simulation and experimental results demonstrate the effectiveness of the proposed control approach in achieving the desired reference signal with minimal tracking error, considering that the actuator and system nonlinearities are unknown. Note to Practitioners—In precision-driven motion applications, the control of the motion system plays a pivotal role in attaining the desired motion profile with exceptional accuracy. Recently, modern actuators have garnered attention from industries and academia as they aim to develop the next generation of motion systems for various advanced applications. For instance, reluctance actuators are designed to drive the wafer scanner in lithography machines, and electrostatic actuators are used to drive the mirror optic systems in smartphones. However, the multiple nonlinearities and position dependency inherent in such actuators, where the mover of the actuator is part of the motion system, introduce unstable behavior, limit performance, and pose challenges for controllers. This paper presents a control approach combining feedforward and output feedback control based on the extended high-gain observer (EHGO). The proposed controller offers several advantages, including enhanced performance of motion systems driven by such actuators and increased robustness by estimating unknown nonlinearities or external disturbances. This results in more accurate and reliable motion profiles, particularly in precision applications. Moreover, the proposed control approach is easy to implement since it does not require adaptation, tuning, or training algorithms and involves fewer controller and observer parameters to design.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".