Contributed by Kay Tancock, k.tancock@elsevier.com
Regular Articles
• Chenhan Zhang, Zhenlei Wang, Xin Wang, Machine learning-based data-driven robust optimization approach under uncertainty, https://doi.org/10.1016/j.jprocont.2022.04.013
• Igor M.L. Pataro, Juan D. Gil, Marcus V. Americano da Costa, José L. Guzmán, Manuel Berenguel, A stabilizing predictive controller with implicit feedforward compensation for stable and time-delayed systems, https://doi.org/10.1016/j.jprocont.2022.04.017
• Timur Bekibayev, Uzak Zhapbasbayev, Gaukhar Ramazanova, Optimal regimes of heavy oil transportation through a heated pipeline, https://doi.org/10.1016/j.jprocont.2022.04.020
• Ryan McCloy, Yifeng Li, Jie Bao, Maria Skyllas-Kazacos, Electrolyte flow rate control for vanadium redox flow batteries using the linear parameter varying framework, https://doi.org/10.1016/j.jprocont.2022.04.021
• Haibin Wu, Yu-Han Lo, Le Zhou, Yuan Yao, Process modeling by integrating quantitative and qualitative information using a deep embedding network and its application to an extrusion process, https://doi.org/10.1016/j.jprocont.2022.04.018
• Yue Fu, Bao Li, Jun Fu, Multi-model adaptive switching control of a nonlinear system and its applications in a smelting process of fused magnesia, https://doi.org/10.1016/j.jprocont.2022.04.009
• Duby Castellanos-Cárdenas, Fabio Castrillón, Rafael E. Vásquez, Norha L. Posada, Oscar Camacho, A new Sliding Mode Control tuning approach for second-order inverse-response plus variable dead time processes, https://doi.org/10.1016/j.jprocont.2022.05.001
• Dazi Li, Fuqiang Zhu, Xiao Wang, Qibing Jin, Multi-objective reinforcement learning for fed-batch fermentation process control, https://doi.org/10.1016/j.jprocont.2022.05.003
• Arash Golabi, Abdelkarim Erradi, Ashraf Tantawy, Towards automated hazard analysis for CPS security with application to CSTR system, https://doi.org/10.1016/j.jprocont.2022.04.008
• Sang Hwan Son, Jong Woo Kim, Tae Hoon Oh, Dong Hwi Jeong, Jong Min Lee, Learning of model-plant mismatch map via neural network modeling and its application to offset-free model predictive control, https://doi.org/10.1016/j.jprocont.2022.04.014
• Francis Gagnon, André Desbiens, Éric Poulin, Jocelyn Bouchard, Pierre-Philippe Lapointe-Garant, Performance of predictive control for a continuous horizontal fluidized bed dryer, https://doi.org/10.1016/j.jprocont.2022.05.007
• Xueyu Li, Qiuwen Luo, Limin Wang, Ridong Zhang, Furong Gao, Off-policy reinforcement learning-based novel model-free minmax fault-tolerant tracking control for industrial processes, https://doi.org/10.1016/j.jprocont.2022.05.006
• Milan Zlatkovikj, Hailong Li, Valentina Zaccaria, Ioanna Aslanidou, Development of feed-forward model predictive control for applications in biomass bubbling fluidized bed boilers, https://doi.org/10.1016/j.jprocont.2022.05.005
• José Matias, Julio P.C. Oliveira, Galo A.C. Le Roux, Johannes Jäschke, Steady-state real-time optimization using transient measurements on an experimental rig, https://doi.org/10.1016/j.jprocont.2022.04.015
• Ryota Uematsu, Shiro Masuda, Manabu Kano, Closed-loop identification of plant and disturbance models based on data-driven generalized minimum variance regulatory control, https://doi.org/10.1016/j.jprocont.2022.05.002
Review Article
• Jinxi Zhang, Fan Guo, Kuangrong Hao, Lei Chen, Biao Huang, Identification of errors-in-variables ARX model with time varying time delay, https://doi.org/10.1016/j.jprocont.2022.04.019
Selected Papers from the 11th IFAC SYMPOSIUM on Advanced Control of Chemical Processes, June 13–16 2021, Venice, Italy
• Lai Wei, Ryan McCloy, Jie Bao, Contraction analysis and control synthesis for discrete-time nonlinear processes, https://doi.org/10.1016/j.jprocont.2022.04.016
• Haeun Yoo, Victor M. Zavala, Jay H. Lee, A dynamic penalty approach to state constraint handling in deep reinforcement learning, https://doi.org/10.1016/j.jprocont.2022.05.004
