Fault-Tolerant Leader-Follower Controller for Uncertain Nonlinear Multi-Agent Systems
Bibliographic record
Abstract
A new distributed adaptive fault-tolerant tracking controller is proposed for consensus control of high-order nonlinear multi-agent systems in the presence of actuator faults and unknown time-varying delays. The system is subject to uncertain disturbances under directed topology. The proposed adaptive method is based on dynamic surface control, where the nonlinear dynamics are unknown, and are approximated by a radial basis function neural network. This system model is subject to delay, fault, uncertainty, disturbances, and singularity of the input signal. The typical constraints on time-delay terms, addressed in the related literature, are relaxed. This relaxation, however, can lead to singularity in the controller. This drawback is addressed by using a specific differentiable function in the control law. It is shown that the generated closed-loop signals are uniformly semi-globally bounded, and the tracking errors converge to a neighborhood of the origin. The effectiveness of the proposed controller is verified by simulations. Note to Practitioners—This work focuses on the distributed control of nonlinear multi-agent time-delay systems in the presence of actuator faults. The proposed control design has potential applications in various real-world network systems such as smart grids, transportation networks, autonomous vehicles, etc. Note that many practical multi-agent systems are subject to time delay and parameter uncertainties, and ignoring these in the control design can negatively impact the system performance and even destabilize it. We demonstrate that the resulting closed-loop system is uniformly semi-globally stable and that the tracking error converges to a neighborhood of the origin. The boundedness assumption on time delay terms, often considered in the multi-agent system literature, is relaxed here. Consequently, the obtained results closely align with practical situations. In addition, the explosion of complexity problem has been avoided by using the dynamic surface control method. The dynamic system represents an unknown-structure dynamic model. This type of dynamic model is applicable to various practical engineering systems. Particularly, robotic systems, chemical reactor systems and spring-mass-damper systems can be described by a model similar to this dynamic system.
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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".