Fault-Tolerant Cooperative Control Design of Multiple Wheeled Mobile Robots
Bibliographic record
Abstract
This brief investigates fault-tolerant cooperative control (FTCC) strategies for multiple differentially driven autonomous wheeled mobile robots (WMRs) in the presence of actuator faults during formation operation. First, for normal/fault-free cases and for preparation to the faults occurrence cases, an integrated approach combining input-output feedback linearization and distributed linear model predictive control techniques is designed and implemented on a team of WMRs to accomplish a formation task. Second, when actuator faults occur in one of the robots of the team, two cases are explicitly considered: 1) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are reassigned to the remaining healthy robots to complete the mission with graceful performance degradation and 2) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure their controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, a fault detection and diagnosis unit using a two-stage Kalman filter to detect and diagnose actuator faults is presented. Then, the FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Moreover, a collision avoidance algorithm based on mechanical impedance principle is proposed to avoid the potential collision between the healthy robots and the faulty ones. Formation operation of the robot team is based on a leader-follower approach, while the control algorithm is implemented in a distributed manner. The results of real experiments demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".