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Distributed fuzzy clustering based association rule mining: design, deployment and implementation

Published in Proceedings of the China Automation Congress, 2021

In this paper, a distributed fuzzy clustering based association rule mining (DFARM) framework is proposed where outside-layer and inside-layer distribution are employed to realize the parallel operation of the whole FARM algorithm.

Recommended citation: Wu, J., Dai, L., Ma, Y., Zou, W., & Xia, Y. (2021, October). Distributed fuzzy clustering based association rule mining: Design, deployment and implementation. In 2021 China Automation Congress (CAC) (pp. 4366-4372). IEEE.
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Cloud-based computational model predictive control using a parallel multiblock ADMM approach

Published in IEEE Internet of Things Journal, 2023

We devise an innovative scheme called cloud-based computational model predictive control (MPC) by using an elaborately designed parallel multiblock alternating direction method of multipliers (ADMMs) algorithm. This novel parallel multiblock ADMM algorithm is tailored to tackle the computational issue of solving a nonconvex problem with nonlinear constraints.

Recommended citation: Dai, L., Ma, Y., Gao, R., Wu, J., & Xia, Y. (2023). Cloud-based computational model predictive control using a parallel multiblock ADMM approach. IEEE Internet of Things Journal, 10(12), 10326-10343.
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Iterative distributed model predictive control for nonlinear systems with coupled non‐convex constraints and costs

Published in International Journal of Robust and Nonlinear Control, 2024

This paper proposes a distributed model predictive control (DMPC) algorithm for dynamic decoupled discrete-time nonlinear systems subject to nonlinear (maybe non-convex) coupled constraints and costs.

Recommended citation: Wu, J., Dai, L., & Xia, Y. (2024). Iterative distributed model predictive control for nonlinear systems with coupled non‐convex constraints and costs. International Journal of Robust and Nonlinear Control, 34(11), 7220-7244.
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Iterative distributed model predictive control for heterogeneous systems with non-convex coupled constraints

Published in Automatica (Regular paper), 2024

This paper investigates the distributed model predictive control (DMPC) problem for multiple dynamically-decoupled heterogeneous linear systems subject to both local state and input constraints and coupled non-convex constraints (e.g., collision avoidance constraints).

Recommended citation: Wu, J., Dai, L., & Xia, Y. (2024). Iterative distributed model predictive control for heterogeneous systems with non-convex coupled constraints. Automatica, 166, 111700.
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Distributed Fuzzy Clustering Based Association Rule Mining: Design, Deployment and Implementation

Published: August 13, 2022

This talk (paper) won the Best Theory Paper Award of China Automation Congress 2021.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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