CONTROLLER design is more troublesome in aerospace systems due to, inter alia, diversity
of mission platforms, convoluted nonlinear dynamics, predominantly strict mission and …

The aim of this paper is to review and to propose a classification of a number of
decentralized, distributed and hierarchical control architectures for large scale systems …

Chemical engineering applications have been a source of challenging optimization
problems for over 50 years. For many chemical process systems, detailed steady state and …

Controller design faces a trade-off between robustness and performance, and the reliability
of linear controllers has caused many practitioners to focus on the former. However, there is …

Integration of real-time optimization and control with higher level decision-making
(scheduling and planning) is an essential goal for profitable operation in a highly …

Widespread application of dynamic optimization with fast optimization solvers leads to
increased consideration of first-principles models for nonlinear model predictive control …

This paper deals with the robustness of Model Predictive Controllers for constrained
uncertain nonlinear systems. The uncertainty is assumed to be modeled by a state and input …

In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …

Min-max model predictive control (MPC) is one of the few techniques suitable for robust
stabilization of uncertain nonlinear systems subject to constraints. Stability issues as well as …

Model Predictive Control (MPC) is an area in rapid development with respect to both
theoretical and application aspects. The former petrochemical applications of MPC were …