Distributed Process Control Using Dissipativity Theory
This talk will give an overview of a non-cooperative distributed control approach (including distributed model predictive control) based on the dissipativity theory. Complex process plants increasingly appear in modern chemical industry due to the considerable economic efficiency that complex and interactive process designs can offer. Due to the wide use of material recycles and heat integration, there are severe interactions between process units, which profoundly alter and complicate plantwide process dynamics. In this approach, a plantwide process is explicitly modelled as a network of interconnected process units (with both physical mass and energy flow and information flow) and controlled by a network of autonomous controllers. The plant-wide process and distributed control system are represented as two interacting process and controller networks, with interaction effects captured by the dissipativity properties of subsystems and network topologies. The plant-wide stability and performance conditions are developed based on global dissipativity conditions, which in turn are translated into the dissipative trajectory conditions that each local MPC must satisfy. This approach is enabled by the use of dynamic supply rates in quadratic difference forms, which capture detailed dynamic system information. Extensions to contraction theory and data-based dissipativity theory will also be discussed.
Dr Jie Bao is a professor at School of Chemical Engineering, UNSW. He is a Process Control expert of international repute, particularly in dissipativity/passivity-based process control. He is the Director of ARC Research Hub for Integrated Energy Storage Systems and also leads the Process Control Research Group. His research interests include dissipativity theory-based process control, networked and distributed control systems, system behavioural theory and control applications in membrane separation, flow batteries, coal preparation and Aluminium smelting. He has published extensively in major process control and chemical engineering journals. He is an Associate Editor of Journal of Process Control (an International Federation of Automatic Control affiliated journal) and Digital Chemical Engineering (an IChemE journal). He also serves on International Federation of Automatic Control Technical Committees: Chemical Process Control (TC6.1); Mining, Mineral and Metal Processing (TC6.2).
Yan Y; Bao J; Huang B, 2023, Distributed Data-driven Predictive Control via Dissipative Behavior Synthesis, IEEE Transactions on Automatic Control, http://dx.doi.org/10.1109/TAC.2023.3298281.
Yan Y; Bao J; Huang B, 2023, On Approximation of System Behavior from Large Noisy Data Using Statistical Properties of Measurement Noise, IEEE Transactions on Automatic Control, http://dx.doi.org/10.1109/TAC.2023.3305191.
Li W; Yan Y; Bao J, 2023, Data-Based Fault Diagnosis via Dissipativity-Shaping, IEEE Control Systems Letters, vol. 7, pp. 484 - 489, http://dx.doi.org/10.1109/LCSYS.2022.3193978
Wei L; McCloy R; Bao J; Cranney J, 2022, Discrete-time contraction constrained nonlinear model predictive control using graph-based geodesic computation, AIChE Journal, vol. 68, http://dx.doi.org/10.1002/aic.17830
Wei L; McCloy R; Bao J, 2022, Contraction analysis and control synthesis for discrete-time nonlinear processes, Journal of Process Control, vol. 115, pp. 58 - 66, http://dx.doi.org/10.1016/j.jprocont.2022.04.016
Xiao G; Yan Y; Bao J; Liu F, 2021, Robust distributed economic model predictive control based on differential dissipativity, AIChE Journal, vol. 67, http://dx.doi.org/10.1002/aic.17198
McCloy RJ; Wang R; Bao J, 2021, Differential dissipativity based distributed MPC for flexible operation of nonlinear plantwide systems, Journal of Process Control, vol. 97, pp. 45 - 58, http://dx.doi.org/10.1016/j.jprocont.2020.11.007
Li W; Yan Y; Bao J, 2020, Dissipativity-based distributed fault diagnosis for plantwide chemical processes, Journal of Process Control, vol. 96, pp. 37 - 48, http://dx.doi.org/10.1016/j.jprocont.2020.10.007
Yan Y; Wang R; Bao J; Zheng C, 2019, Robust distributed control of plantwide processes based on dissipativity, Journal of Process Control, vol. 77, pp. 48 - 60, http://dx.doi.org/10.1016/j.jprocont.2019.02.002
Wang R; Zhang X; Bao J, 2019, A self-interested distributed economic model predictive control approach to battery energy storage networks, Journal of Process Control, vol. 73, pp. 9 - 18, http://dx.doi.org/10.1016/j.jprocont.2018.11.003
Wang R; Bao J, 2017, Distributed plantwide control based on differential dissipativity, International Journal of Robust and Nonlinear Control, vol. 27, pp. 2253 - 2274, http://dx.doi.org/10.1002/rnc.3681
Tippett MJ; Bao J, 2014, Control of plant-wide systems using dynamic supply rates, Automatica, vol. 50, pp. 44 - 52, http://dx.doi.org/10.1016/j.automatica.2013.09.028
Hioe D; Bao J; Hudon N, 2013, Interaction analysis and geometric interconnection decoupling for networks of process systems, AIChE Journal, vol. 59, pp. 2795 - 2809, http://dx.doi.org/10.1002/aic.14059
Hioe D; Bao J; Ydstie BE, 2013, Dissipativity analysis for networks of process systems, Computers and Chemical Engineering, vol. 50, pp. 207 - 219, http://dx.doi.org/10.1016/j.compchemeng.2012.11.010
Tippett MJ; Bao J, 2013, Dissipativity based distributed control synthesis, Journal of Process Control, vol. 23, pp. 755 - 766, http://dx.doi.org/10.1016/j.jprocont.2013.03.004
Tippett MJ; Bao J, 2013, Distributed model predictive control based on dissipativity, AIChE Journal, vol. 59, pp. 787 - 804, http://dx.doi.org/10.1002/aic.13868