arXiv subjects

Ganghui Cao

Publications and source records attributed to Ganghui Cao.

3 recordsLinked to original sources

arXiv2026

Distributed Omniscient Observers for Multi-Agent Systems: Design and Applications

This paper proposes distributed omniscient observers for both heterogeneous and homogeneous linear multi-agent systems, such that each agent can correctly estimate the states of all agents. The observer design is based on local input-output information available to each agent, and knowledge of the global communication graph among agents is not necessarily required. The proposed observers can contribute to distributed Nash equilibrium seeking in multi-player games and the emergence of self-organized social behaviors in artificial swarms. Simulation results demonstrate that artificial swarms can emulate animal social behaviors, including sheepdog herding and honeybee dance-based navigation.

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arXiv2025

Distributed Observer and Controller Design for Linear Systems: A Separation-Based Approach

This paper investigates the problem of consensus-based distributed control of linear time-invariant multi-channel systems subject to unknown inputs. A distributed observer-based control framework is proposed, within which observer nodes and controller nodes collaboratively perform state estimation and control tasks. Consensus refers to a distributed cooperative mechanism by which each observer node compares its state estimate with those of neighboring nodes, and use the resulting discrepancies to update its own state estimate. One key contribution of this work is to show that the distributed observers and the distributed controllers can be designed independently, which parallels the classical separation principle. This separability within the distributed framework is enabled by a discontinuous consensus strategy and two adaptive algorithms developed specifically for handling the unknown inputs. Theoretical analysis and numerical simulation results demonstrate the effectiveness of the proposed framework in achieving state estimation, stabilization, and tracking control objectives.

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arXiv2025

Partial Excitation in Parameter Learning

This paper investigates parameter learning problems under Partial Persistent Excitation (PPE). The PPE condition is a rank-deficient, and therefore, a more general evolution of the well-known Persistent Excitation (PE) condition. Under the PPE condition, a proposed online algorithm is able to calculate the PE and non-PE subspaces, and finally gives an optimal parameter estimate in the sense of least squares. In particular, the learning error within the PE subspace exponentially converges to zero in the noise-free case. The PPE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary PPE condition. This protocol allows each local estimator to operate locally in its PE subspace, and reach a consensus with neighbors in its non-PE subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper.

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