Decentralized Receding Horizon Control for Cooperative Multiple Vehicles Subject to Communication Delay
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
N this paper, a new approach is proposed for the decentralized receding horizon control (DRHC) of multiple cooperative vehicles with the possibility of communication failures leading to large intervehicle communication delay. Such large communication delays can lead to poor performance and even instability. The neighboring vehicles exchange their predicted trajectories at each sampletimetomaintainthecooperationobjectives.Itisassumedthat the communication failure is partial in nature, which in turn leads to large communication delays of the exchanged trajectories. The proposedfault-tolerantDRHCisbasedontwoextensionsofexisting work for the case of large communication delays. The first contributionisthedevelopment ofanewDRHC approachthat estimates thetrajectoryoftheneighboringvehiclesforthetailoftheprediction horizon, which would otherwise not be available due to the communication delay. In this approach, the tail of the cost function is estimated by adding extra decision variables in the cost function. A relatively small amount of existing work has investigated the implementation issues associated with exchange of trajectory information, but so far no work has proposed a tail estimation process to compensate for large delays. For instance, in [1–3], no prediction or estimation for the trajectory of neighboring vehicles is performed, and it is assumed that the neighboring vehicles remain at the last delayed states broadcasted by them. Such assumptions may yield poor performance for large communication delays because the constant state vector is not a good estimation of a trajectory of states in general. Similar issues are also investigated in [4,5]. The second contribution of this paper is an extension of the tubebased model predictive control (MPC) approach [6,7] for the case of thelargecommunicationdelaysinordertoguaranteethesafetyofthe fleet against possible collisions during formation control problems. The concept of the tube MPC [or tube receding horizon control (RHC)] in existing work [6,7] is normally used to calculate a robust bound on the states due to system uncertainty, whereas in this paper, the approach is used to calculate bounds that arise from large communication delays of the exchanged neighbor trajectories. The proposed algorithms in this paper are presented in the context of fault-tolerant control, as the communication delay/break may occur due to any failure and malfunction in the communication devices. Some examples of communication failures for the team of cooperative vehicles can be found in [8–10]. In [8], the wireless communicationpacketloss/delayisconsidered;oncethepacketloss/ delay occurs, the previous available trajectory of the faulty unmanned aerial vehicle (UAV) is extrapolated to predict the future reference trajectory. Also, in [9], the communication failure in formation flight of multiple UAVs leads to a break in the communicated messages that forces the fleet to redefine the communication graph. This paper is organized as follows. Section II deals with a general formulation of the decentralized receding horizon controller, and the corresponding algorithm for a fault-free (delay-free) condition. In Section III, a faulty condition is first defined, and a reconfigurable fault-tolerant controller is developed. A safety guarantee method forthefaultyconditionisalsodevelopedbasedontheconceptoftube RHC. In Section IV, the proposed algorithms are tested through simulation of a leaderless formation controller for a fleet of unmanned vehicles.
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 | 0.000 | 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 | 0.000 |
| 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 it