Adaptive chattering-free sliding mode control design using fuzzy model of the system and estimated uncertainties and its application to robot manipulators
Bibliographic record
Abstract
This paper presents design and implementation of a novel adaptive chattering-free sliding mode control (ACFSMC) scheme and its application to motion control of robot manipulators. Due to the presence of complex phenomena such as large flexibility, model uncertainties, and external disturbances a robust and adaptive control is required to control the motion of the robots. This paper presents the design of an adaptive chattering-free sliding mode control using two adaptation mechanism namely: a fast and performance-based online estimation of uncertainties, which is constructed based on the dynamic behaviour of a sliding function; and an adaptive fuzzy model of the robot, which is constructed using a systematic fuzzy modelling method and from input-output data of the robot through system identification. These two adaptation mechanism can be interpreted as the integration of fast response to immediate feedback information and the response based on the knowledge that has already been built into the fuzzy model of the controller, which is the model-based component of the ACFSMC. The global stability and robustness of the proposed controller are established using Lyapunov's approach and fundamentals of sliding mode theory. Based on the simulations and experimental results, the proposed controller performs remarkably well in comparison to SMC with boundary layer and the high gain proportional-integral-derivative (PID) controllers in terms of the tracking error convergence and robustness against uncertainties.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".