PSO-based nonlinear model predictive planning and discrete-time sliding tracking control for uncertain planar underactuated manipulators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Control of planar underactuated manipulators (PUM) with unknown parameter perturbations and external disturbances is still a challenging task due to their complex and peculiar characteristics. The research on it is significant in the view of wide applications in practice. In this paper, taking an uncertain 3-degree of freedom PUM with a free first joint as a benchmark example, we discuss its position control issue. Specifically, an integrated control method is developed, including the nonlinear model prediction control (NMPC) based on an improved particle swarm optimisation (PSO) algorithm and the discrete-time fast terminal sliding mode (FTSM) control. The PSO-based NMPC is proposed for planning discrete trajectories of the active joint angles in real time, along which the manipulator end-point can reach the desired position. Then the discrete-time FTSM controllers are designed to keep the active joints tracking the discrete trajectories, where the uncertainties related to the active links/joints are estimated by time delay estimation method. Besides, the influence of the uncertainties related to the free link/joint on the system can be made up by the NMPC in real time. It is confirmed via simulations that the above method can achieve the accurate positioning of such an uncertain manipulator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 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 it