| 1. |
- Benalcazar, Diego R., et al.
(författare)
-
Average Consensus With Error Correction
- 2024
-
Ingår i: IEEE Control Systems Letters. - 2475-1456. ; 8, s. 115-120
-
Tidskriftsartikel (refereegranskat)abstract
- We propose a novel method for achieving the average consensus in a distributed manner while dealing with communication compression. While it is widely recognized that distributed consensus algorithms with compression can falter due to compression-error-induced divergences, our approach integrates an error correction step to guarantee convergence towards an approximate average consensus across any bounded compression function. Significantly, with our error correction mechanism, we can achieve convergence to a solution of arbitrarily high accuracy, irrespective of how crude the compression is in a fully distributed setting. Additionally, we quantify the convergence rate and provide upper bounds for the estimation error based on the spectral properties of the underlying communication network. Simulation results validate the scalability and efficacy of our proposed algorithm.
|
|
| 2. |
- Charitidou, Maria, et al.
(författare)
-
Distributed MPC With Continuous-Time STL Constraint Satisfaction Guarantees
- 2024
-
Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1456. ; 8, s. 211-216
-
Tidskriftsartikel (refereegranskat)abstract
- In this letter a distributed model predictive control scheme (dMPC) is proposed for a multi-agent team that is subject to a set of time-constrained spatial tasks encoded in Signal Temporal Logic (STL). Here, the agents are subject to both individual and collaborative STL tasks. In order to ensure the satisfaction of the collaborative tasks while avoiding the computational burden of a centralized problem, we propose a sequential dMPC scheme and show the recursive feasibility property of the framework given appropriately designed terminal ingredients. The resulting MPC problems are solved in discrete-time yet continuous-time satisfaction of the STL tasks is ensured with appropriate tightening of the constraint sets.
|
|
| 3. |
- Chen, Xiaomeng, et al.
(författare)
-
Communication-Efficient and Differentially-Private Distributed Nash Equilibrium Seeking With Linear Convergence
- 2024
-
Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1456. ; 8, s. 1787-1792
-
Tidskriftsartikel (refereegranskat)abstract
- The distributed computation of a Nash equilibrium (NE) for non-cooperative games is gaining increased attention recently. Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns. Traditional approaches often address these critical concerns in isolation. This letter introduces a unified framework, named CDP-NES, designed to improve communication efficiency in the privacy-preserving NE seeking algorithm for distributed non-cooperative games over directed graphs. Leveraging both general compression operators and the noise adding mechanism, CDP-NES perturbs local states with Laplacian noise and applies difference compression prior to their exchange among neighbors. We prove that CDP-NES not only achieves linear convergence to a neighborhood of the NE in games with restricted monotone mappings but also guarantees ε-differential privacy, addressing privacy and communication efficiency simultaneously. Finally, simulations are provided to illustrate the effectiveness of the proposed method.
|
|
| 4. |
- Gaetan, Elisa, et al.
(författare)
-
Scenario-Tree Model Predictive Control for Vehicle Interactions in Highway Setting
- 2024
-
Ingår i: IEEE Control Systems Letters. - 2475-1456. ; 8, s. 1162-1167
-
Tidskriftsartikel (refereegranskat)abstract
- In this letter, we present a modeling and control design framework for modeling and influencing the drivers' decisions in highway scenarios using one or more vehicles as actuators. Our approach relies on a driver's decision-making model that is used to design a scenario-tree model predictive controller, which calculates acceleration and lane change commands for a set of controlled vehicles. We illustrate our modeling and control framework in a two-lane highway example, with two vehicles, one autonomous and one human-driven. Results from numerical simulations demonstrate how our approach can efficiently influence the lane changes of one vehicle using the other as a control actuator.
|
|
| 5. |
- Huo, Wei, et al.
(författare)
-
Compression-Based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games
- 2024
-
Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1456. ; 8, s. 886-891
-
Tidskriftsartikel (refereegranskat)abstract
- This letter explores distributed aggregative games in multi-agent systems. Current methods for finding distributed Nash equilibrium require players to send original messages to their neighbors, leading to communication burden and privacy issues. To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression. Our theoretical analysis shows that the algorithm guarantees convergence accuracy, even with aggressive compression errors used to protect privacy. We prove that the algorithm achieves differential privacy through a stochastic quantization scheme. Simulation results for energy consumption games support the effectiveness of our approach.
|
|
| 6. |
- Mattsson, Per, 1984-, et al.
(författare)
-
On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches
- 2024
-
Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1456. ; 8, s. 796-801
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model. In this letter, we show their equivalence to a (relaxed) indirect approach, allowing us to reformulate direct methods in terms of estimated parameters and covariance matrices. This allows us to provide further insights into how these direct predictive control methods work, showing that, for unconstrained problems, the direct methods are equivalent to subspace predictive control with a reduced weight on the tracking cost, and analyzing the impact of the data length on tuning strategies. Via a numerical experiment, we also illustrate why the performance of direct DDPC methods with fixed regularization tends to degrade as the number of training samples increases.
|
|
