Model-based Sensor-based Velocity Control Of Teleoperated Heavy-duty Hydraulic Machines
Bibliographic record
Abstract
This paper presents a method for motion control of a class of heavy-duty hydraulic machines in a resolved teleoperation mode. A model-based sensor-based algorithm is proposed which is applied in conjunction with the closed-loop components. The algorithm is a feedforward load compensating scheme which uses the measured hydraulic line pressures along with an appropriate portion of the hydraulic model to control the joint velocities. The scheme incorporates a logic system which, according to the states relevant to the machine task, decides on the minimum portion of the hydraulic model to be used. Inclusion of this algorithm eliminates most of the effects of loading, coupled actuation and single joint power limitations, which allows easier implementation of the closed-loop part. A knowledge of some hydraulic parameters is the only requirement of this scheme. No knowledge about the structure or loading is necessary. I. INTRODUCTION There exist many industrial tasks that are performed in di- verse and unstructured surroundings. Examples of these tasks can be found in the forest, mining and construction industries These industries utilize many heavy-duty machines (such as excavators, feller bunchers or log loaders) that have linked me- chanical structures, actuated through coupled and complex hydraulic systems. The present control of these machines is based on individually actuating each link by an operator which requires significant visual feedback, judgement and skill. These machines do not benefit from computer-assisted con- trols; however, they have the potential to be automated. This paper presents a novel application of teleoperation to such industrial machines. In teleoperation, the operator's commands are passed to a computer which is interfaced to the machine and performs the actual control. At least three benefits can be achieved. Firstly, the human's operation of the machine can be coordi- nated. Secondly, the computer can check for incipient machine failures. Thirdly, repetitive operations can be automated un- der supervision of the human.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".