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Guo-Ping Liu

Researcher at Wuhan University

Publications -  615
Citations -  20111

Guo-Ping Liu is an academic researcher from Wuhan University. The author has contributed to research in topics: Model predictive control & Control system. The author has an hindex of 66, co-authored 545 publications receiving 17692 citations. Previous affiliations of Guo-Ping Liu include Beijing Institute of Technology & University of Nottingham.

Papers
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Data-Driven Leader-Follower Output Synchronization for Networked Non-Linear Multi-Agent Systems with Switching Topology and Time-Varying Delays

TL;DR: A novel data-driven consensus protocol based on model free adaptive control is proposed, where the reference input of each follower is designed to be the time-varying average of the neighboring agents’ outputs.
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Output consensus of networked multi-agent systems with time-delay compensation scheme

TL;DR: A novel distributed protocol is proposed with the predictions of agents outputs at current time, instead of available outdated data, to compensate for the network delay actively in DTMASs with heterogeneous agents and a constant communication delay.
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Architecture design for internet-based control systems

TL;DR: An approach to writing requirements specifications for Internet-based control systems, from which architectures can be derived, is described in terms of a functional model, which is then extended to form an information architecture.
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Robust control design based on parametric eigenvalue assignment

TL;DR: The controller designed via state-feedback eigenstructure assignment is analytically expressed by new totally free parameters for the cases where the sets of closed- and open-loop eigenvalues intersect, and the case where those do not.
Proceedings ArticleDOI

Partial eigenstructure assignment by state feedback: A complete parametric approach

TL;DR: With the help of a complete parametric solution to the generalised Sylvester matrix equation AV+BW=VF, simple, complete, parametric expressions are established for the assigned closed-loop eigenvectors and the feedback gain.