Control Performance Management in Industrial Automation provides a coherent and self-
contained treatment of a group of methods and applications of burgeoning importance to the …

In this paper, we discuss the model predictive control algorithms that are tailored for
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …

This paper provides an overview of commercially available model predictive control (MPC)
technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A …

Receding Horizon Control introduces the essentials of a successful feedback strategy that
has emerged in many industrial fields: the process industries in particular. Receding horizon …

An efficient Newton-type scheme for the approximate on-line solution of optimization
problems as they occur in optimal feedback control is presented. The scheme allows a fast …

In this paper, we present a neurodynamic approach to model predictive control (MPC) of
unknown nonlinear dynamical systems based on two recurrent neural networks (RNNs). The …

Gaussian process models provide a probabilistic non-parametric modelling approach for
black-box identification of non-linear dynamic systems. The Gaussian processes can …

The ability of a model-based real-time optimization (RTO) scheme to converge to the plant
optimum relies on the ability of the underlying process model to predict the plant's necessary …

A non-linear model predictive controller (NMPC) was investigated as a route to delivering
improved product quality, batch to batch reproducibility and significant cost reductions by …

Control and automation in its broadest sense plays a fundamental role in process industries.
Control assures stability of technologies, disturbance attenuation, safety of equipment and …