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Shiquan Zhao

Researcher at Ghent University

Publications -  12
Citations -  90

Shiquan Zhao is an academic researcher from Ghent University. The author has contributed to research in topics: Model predictive control & Control theory. The author has an hindex of 5, co-authored 12 publications receiving 56 citations. Previous affiliations of Shiquan Zhao include Harbin Engineering University & Core Laboratories.

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Journal ArticleDOI

Effect of Control Horizon in Model Predictive Control for Steam/Water Loop in Large-Scale Ships

TL;DR: This paper presents an extensive analysis of the properties of different control horizon sets in an Extended Prediction Self-Adaptive Control (EPSAC) model predictive control framework, and concludes that specific tuning of control horizons outperforms the case when only a single valued control horizon is used for all the loops.
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A robust PID autotuning method applied to the benchmark PID18

TL;DR: A proportional-integral-derivative autotuning control strategy that is based on a single sine test, with user-defined robustness margins guaranteed, is presented and applied to the benchmark system presented at the 3 rd IFAC Conference on Advances in Proportional-Integral-Derivative Control.
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A Low Computational Cost, Prioritized, Multi-Objective Optimization Procedure for Predictive Control Towards Cyber Physical Systems

TL;DR: This work proposes a sequential implementation of a multi-objective optimization procedure suitable for industrial settings and cyber physical systems with strong interaction dynamics, and uses an Extended Prediction self-adaptive Control strategy with prioritized objectives.
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IMC based PID Control Applied to the Benchmark PID18

TL;DR: An Internal Model Control based proportional-integral-derivative (PID) control is presented and evaluated on the benchmark system presented at the 3 rd IFAC Conference on Advances in Proportional-Integral-Derivative Control.
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Nonlinear Predictive Control Applied to Steam/Water Loop in Large Scale Ships

TL;DR: It is concluded that specific tuning of prediction horizons based on loop’s dynamic outperforms the case when a trade-off is made and a single valued prediction horizon is used for all the loops.