Feedback passivation plus tracking-error-based multivariable control for a class of free-radical polymerisation reactors

This paper proposes a tracking-error-based multivariable control to stabilise a nonlinear system at the desired trajectory (including the open-loop unstable equilibrium manifold). The control approach is developed on the basis of feedback passivation and then applied to stabilise globally exponentially a class of free-radical polymerisation reactors. More precisely, under certain conditions the system dynamics can be rendered strictly input/output passive through the use of an appropriate input coordinate transformation. A canonical form related to the so-called port-Hamiltonian representation of passive system is consequently derived and provides physical interpretations such as dissipative/non-dissipative term and supply rate. A feedback law based on tracking-error is then designed for the global exponential stabilisation at a reference trajectory passing through the desired set-point. The theoretical developments are illustrated for polystyrene production in a continuous stirred tank reactor. Numerical simulations show that the system trajectory converges globally exponentially to the reference trajectory despite effects of disturbance. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems