First-Order Continuous Adaptive Sliding Mode Control for Robot Manipulators with Finite-Time Convergence of Trajectories to Real Sliding Mode
Bibliographic record
Abstract
In this paper the design, analysis and implementation of improved first-order continuous adaptive sliding mode control (CASMC) based on online estimation of the lumped time-varying uncertainties for tracking control of the robot manipulators is presented. The proposed method allows to address the main drawbacks of conventional sliding mode control: the chattering phenomenon, and the requirement for a priori knowledge of the bounds of the uncertainties, and also the chattering problem associated with adaptive discontinuous sliding mode controllers, while the robustness property of the conventional sliding mode control is preserved. Furthermore, in the previously published version of the controller [1], the estimate of robot inertia matrix is needed to realize the adaptive component of the control law. In this work, the skew-symmetry property (passivity property) of robot dynamic is used to eliminate that requirement. The global stability and robustness of the proposed controller are established in the presence of time-varying uncertainties using Lyapunov's approach and fundamentals of sliding mode theory. The robustness is achieved without knowing the bound of uncertainties. The dynamic model of a two-degrees of freedom (2-DOF) rigid robot is used for simulation study and a 2-DOF flexible-link robot is used as an experimental test-bed to evaluate the performance, and robustness of the controller. Based on the simulations and experimental results, the proposed controller performs remarkably well in terms of the tracking error convergence, estimation of lumped uncertain parameter. And it is robust against un-modeled dynamics and external disturbances.
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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) | 0.001 | 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".