Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Localized Data-driven Consensus Control

Zeze Chang, Junjie Jiao, Zhongkui Li

TL;DR

The paper tackles leader-follower multi-agent consensus with unknown linear dynamics by enabling each follower to compute its own local control gain from local data (localized data-driven control). It develops noise-free and noisy-data consensus protocols based on low-dimensional LMIs, and extends to schemes where only the leader provides data, with synchronization ensuring global consensus. Key contributions include (1) distributed, data-driven gains avoiding centralized design, (2) robust handling of bounded noise via the S-procedure and informativity, and (3) scalable LMIs that maintain tractability for large networks. The methods are validated through simulations demonstrating convergence under both ideal and noisy data, highlighting practical applicability for distributed control without full system identification.

Abstract

This paper considers a localized data-driven consensus problem for leader-follower multi-agent systems with unknown discrete-time agent dynamics, where each follower computes its local control gain using only their locally collected state and input data. Both noiseless and noisy data-driven consensus protocols are presented, which can handle the challenge of the heterogeneity in control gains caused by the localized data sampling and achieve leader-follower consensus. The design of these data-driven consensus protocols involves low-dimensional linear matrix inequalities. In addition, the results are extended to the case where only the leader's data are collected and exploited. The effectiveness of the proposed methods is illustrated via simulation examples.

Localized Data-driven Consensus Control

TL;DR

The paper tackles leader-follower multi-agent consensus with unknown linear dynamics by enabling each follower to compute its own local control gain from local data (localized data-driven control). It develops noise-free and noisy-data consensus protocols based on low-dimensional LMIs, and extends to schemes where only the leader provides data, with synchronization ensuring global consensus. Key contributions include (1) distributed, data-driven gains avoiding centralized design, (2) robust handling of bounded noise via the S-procedure and informativity, and (3) scalable LMIs that maintain tractability for large networks. The methods are validated through simulations demonstrating convergence under both ideal and noisy data, highlighting practical applicability for distributed control without full system identification.

Abstract

This paper considers a localized data-driven consensus problem for leader-follower multi-agent systems with unknown discrete-time agent dynamics, where each follower computes its local control gain using only their locally collected state and input data. Both noiseless and noisy data-driven consensus protocols are presented, which can handle the challenge of the heterogeneity in control gains caused by the localized data sampling and achieve leader-follower consensus. The design of these data-driven consensus protocols involves low-dimensional linear matrix inequalities. In addition, the results are extended to the case where only the leader's data are collected and exploited. The effectiveness of the proposed methods is illustrated via simulation examples.
Paper Structure (10 sections, 9 theorems, 52 equations, 5 figures)

This paper contains 10 sections, 9 theorems, 52 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Localized Data-driven Control with Noise-free Data
  3. Problem formulation
  4. Data-driven control design
  5. Localized Data-driven Control with Noisy Data
  6. Problem formulation
  7. Data-driven control design
  8. Localized Data-driven Control with Leader's Data
  9. Simulation Results
  10. Conclusion

Key Result

Theorem 1

Let Assumptions asp 1-asp 3 hold. Suppose that there exists $\Gamma_i$ such that where $Q_i>0$ is a constant matrix. Then, the initial feedback gain matrix $K_i(0)$ of the $i$th agent can be calculated as $K_i(0)=U_{i-}\Gamma_i(X_{i-}\Gamma_i)^{-1}$.

Figures (5)

  • Figure 1: The communication topology.
  • Figure 2: Trajectories of agents on the X-axis for the case of noiseless data.
  • Figure 3: Trajectories of agents on the Y-axis for the case of noiseless data.
  • Figure 4: Trajectories of agents on the X-axis for the case of noisy data.
  • Figure 5: Trajectories of agents on the Y-axis for the case of noisy data.

Theorems & Definitions (20)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Lemma 1
  • Lemma 2
  • proof
  • Remark 1
  • Lemma 3
  • Theorem 3
  • ...and 10 more