For these kinds of processes traditional linear and empirical model predictive control was considered less suitable.

The aim of the company was to commercialize the results on model predictive control that grew out of these research projects.

In process control, the superposition principle is used in model predictive control.

Specific control techniques are available to solve the problem: model predictive control (see later), and anti-wind up systems.

Model predictive control systems are designed using specialized computer-aided-design software and empirical mathematical models of the system to be controlled.

See Model predictive control.

Many other researchers provided solutions using concepts from several control areas such as robust control, optimal stochastic control, model predictive control, fuzzy logic etc.

ISAT was first proposed by Stephen B. Pope for computational reduction of turbulent combustion simulation and later extended to model predictive control.

Optimisation control schemes include: linear-quadratic regulator design (LQR), model predictive control (MPC) and eigenstructure assignment methods.

High-level controllers such as Model predictive control (MPC) or Real-Time Optimization (RTO) employ mathematical optimization.

The company grew out of industrial research projects on model predictive control (MPC) conducted at the department for Engineering cybernetics at SINTEF in the late 1990s.

The online case being model predictive control, where the real-time simulation results are used to predict the changes that would occur for a control input change, and the control parameters are optimised based on the results.

Cybernetica delivers systems for model predictive control (MPC) and soft-sensing, as well as performing research and problem-solving for hire within the field of process control, for customers within polymer, metal and petroleum industry.

Model predictive control products to control the settings of industrial plants in real-time with the aim of maximizing throughput or some economic objective, to ensure that process constraints are met or to achieve a more smooth and stable process by rejecting disturbances.

Nonlinear Model predictive control an in-house developed suite for nonlinear model predictive control can be applied to control processes with strong nonlinearities and to batch processes, for instance polymer reactors [8].

Nonlinear Model Predictive Control, or NMPC, is a variant of model predictive control (MPC) that is characterized by the use of nonlinear system models in the prediction.

Multivariable Model predictive control (MPC) is a popular technology, usually deployed on a supervisory control computer, that identifies important independent and dependent process variables and the dynamic relationships (models) between them, and uses matrix-math based control and optimization algorithms, to control multiple variables simultaneously.