Modelling and Control of Hydraulic and Electrical Actuation Systems for Aerospace Systems
Bibliographic record
Abstract
Modelling and analysis of actuation system is often a prerequisite for robotics design and control. The premise of this work involves mathematical modelling and control system design of actuation systems for decentralized control systems. Two types of actuation systems for two varying applications are considered for the scope of this research. Hydraulic actuation modelling is performed in a modular and independent approach for a robotic application. The sample mechanism consists of a Winglet Cant mechanism with two geometrically different parallel kinematic chains coupled together to the same end-effector actuated using hydraulic actuators. The rotation of the end-effector is its single degree of freedom of motion. The hydraulic modelling is performed using linear modelling for the two actuators responsible for providing the motion for the morphing winglet and non-linear modelling for the dynamic loading system responsible for simulating the load on the winglet in-motion to ensure the structural integrity of the mechanism. Control schemes are proposed for each actuation system independently based on the modelled system characteristics. Through experimentation, the validation of the modelled systems and control scheme is performed. Electrical actuation control is addressed for a cabin comfort application with focus on integration of AI-based vision system for dual axis gimbal motion. With the goal of moving target active noise control, a novel control strategy for electrically actuated gimbal speaker system using sequential control is presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".