Optimal Force Allocation and Position Control of Hybrid Pneumatic–Electric Linear Actuators
Bibliographic record
Abstract
Hybrid pneumatic–electric actuators (HPEAs) are redundant actuators that combine the large force, low bandwidth characteristics of pneumatic actuators with the large bandwidth, small force characteristics of electric actuators. It has been shown that HPEAs can provide both accurate position control and high inherent safety, due to their low mechanical impedance, making them a suitable choice for driving the joints of assistive, collaborative, and service robots. If these characteristics are mathematically modeled, input allocation techniques can improve the HPEA’s performance by distributing the required input (force or torque) between the redundant actuators in accordance with each actuator’s advantages and limitations. In this paper, after developing a model for a HPEA-driven system, three novel model-predictive control (MPC) approaches are designed that solve the position tracking and input allocation problem using convex optimization. MPC is utilized since the input allocation can be embedded within the motion controller design as a single optimization problem. A fourth approach based on conventional linear controllers is included as a comparison benchmark. The first MPC approach uses a model that includes the dynamics of the payload and pneumatics; and performs the motion control using a single loop. The latter methods simplify the MPC law by separating the position and pressure controllers. Although the linear controller was the most computationally efficient, it was inferior to the MPC-based controllers in position tracking and force allocation performance. The third MPC-based controller design demonstrated the best position tracking with RMSE of 46%, 20%, and 55% smaller than the other three approaches. It also demonstrated sufficient speed for real-time operation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".