Deep Learning-based Robot Control using Recurrent NeuralNetworks (LSTM; GRU) and Adaptive Sliding Mode Control
Bibliographic record
Abstract
A phenomenal increase in computational power made deep learning possible for real-time applications in recent years.Nonlinearity, external disturbances, and robustness are significant challenges in robotics.To overcome these challenges, robust adaptive control is needed, which requires manipulator inverse dynamics.Deep Learning can be used to construct the inverse dynamic of a manipulator.In this paper, robust adaptive motion control is developed by effectively combining existing adaptive sliding mode controller (ASMC) with Recurrent Neural Network such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).A supervised learning approach is applied to train the LSTM and GRU model, which replaced the inverse dynamic model of a manipulator in model-based control design.The LSTM-based inverse dynamic model constructed using input-output data obtained from a simulation of a dynamic model of the two-links robot.The deep-learning-based controller applied for trajectory tracking control, and the results of the proposed Deep Learning-based controller are compared in three different scenarios: ASMC only, LSTM or GRU only, and LSTM or GRU with ASMC (with and without disturbance) scenario.The primary strategy of designing a controller with LSTM or GRU is to get better generalization, accuracy enhancement, compensate for fast time-varying parameters and disturbances.The experimental results depict that without tuning parameters proposed controller performs satisfactorily on unknown trajectories and disturbances.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".