By Professor Jie Bao

Preprints

Liao Y; Wei L; Skyllas-Kazacos M; Bao J, 2026, Modelling, Experimental Validation and Control of Shunt Pipe Electrolyte Rebalancing for Vanadium Flow Batteries, http://dx.doi.org/10.2139/ssrn.6579662

Wong C-J; Larkin AA; Bao J; Skyllas-Kazacos M; Welch BJ; Ahli N; Faraj M; Mahmoud M, 2025, Optimisation of Power Modulation for Hall-Héroult Cells: Process Operability and Constraints as Virtual Energy Storage, http://dx.doi.org/10.48550/arxiv.2511.07893

Tang JW; Yan Y; Bao J; Huang B, 2024, Big Data-driven Control of Nonlinear Processes through Dynamic Latent Variables using an Autoencoder, http://dx.doi.org/10.36227/techrxiv.171710237.79956036/v1

Yan Y; Bao J; Huang B, 2023, Distributed Data-driven Predictive Control via Dissipative Behavior Synthesis, http://dx.doi.org/10.1109/TAC.2023.3298281

Wei L; McCloy R; Bao J, 2022, Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks, http://dx.doi.org/10.48550/arxiv.2105.05432

McCloy R; Wei L; Bao J, 2022, A Contraction-constrained Model Predictive Control for Nonlinear Processes using Disturbance Forecasts, http://dx.doi.org/10.48550/arxiv.2205.04033

Wei L; McCloy R; Bao J, 2022, Adaptive Contraction-based Control of Uncertain Nonlinear Processes using Neural Networks, http://dx.doi.org/10.48550/arxiv.2201.12816

Wei L; McCloy R; Bao J, 2022, Contraction Analysis and Control Synthesis for Discrete-time Nonlinear Processes, http://dx.doi.org/10.48550/arxiv.2112.04699

McCloy R; Li Y; Bao J; Skyllas-Kazacos M, 2022, Electrolyte Flow Rate Control for Vanadium Redox Flow Batteries using the Linear Parameter Varying Framework, http://dx.doi.org/10.48550/arxiv.2201.12812

Wei L; McCloy R; Bao J; Cranney J, 2022, Discrete-time Contraction Constrained Nonlinear Model Predictive Control using Graph-based Geodesic Computation, http://dx.doi.org/10.22541/au.164892424.43881718/v1

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