Torque control design of nonholonomic mobile robots using a neural network-based approach
Bibliographic record
Abstract
In this thesis, a novel control algorithm is proposed for a nonholonomic mobile robot with completely unknown robot dynamics and subject to bounded unknown disturbances. By taking advantage of the robot regressor dynamics, the neural network assumes a single layer structure. The learning algorithm is derived from Lyapunov stable analysis, which is much simpler than most commonly used neural network learning algorithms. The control algorithm is computationally 'efficient' resulting from the simple neural network structure and its simple learning rule. The proposed controller is capable of achieving precise motion control of a nonholonomic mobile robot through the on-line learning ability. The stability of the proposed controller is proved using a Lyapunov stability theory. In addition, the proposed controller is extended to a mobile robot with unknown kinematic parameters, where the linear and angular velocities are chosen as the velocity control input. The extended controller is capable of dealing with completely unknown kinematics and dynamics parameters of the robot system. One neural network is designed to learn both the dynamics and kinematic parameters. Furthermore, a novel torque controller is proposed for nonholonomic mobile robots with obstacle avoidance. By introducing an obstacle torque in the controller, the proposed controller is capable of driving the robot to its target and avoiding obstacles in various environments. Both the neural network structure and its learning algorithm are simple. The controller is guaranteed to be stable and converge by a Lyapunov stability theory, subject to unmodeled unstructured disturbance. No prior information of the environment is needed, all the environment information needed can be obtained from the on-board robot sensors with a limited visibility range.
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.
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 | 0.001 | 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 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".