Stochastic Model Predictive Control using Initial State Optimization

Stochastic Model Predictive Control: State space methods

1 Performance objective and closed-loop convergence 1 1.1 Stochastic system models . . . . . . . . . . . . . . . . . . . 1 1.2 Performance cost . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Cost evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Unconstrained optimal control . . . . . . . . . . . . . . . . . 12 1.5 Receding horizon control, stability and convergence . . ...

Stochastic Model Predictive Control

Model Predictive Control (MPC) is a control strategy that has been used successfully in numerous and diverse application areas. The aim of the present article is to discuss how the basic ideas of MPC can be extended to problems involving random model uncertainty with known probability distribution. We discuss cost indices, constraints, closed loop properties and implementation issues.

Model Predictive Control Using Polynomial Optimization

Sag Mill Optimization Using Model Predictive Control

Semi-Autogenous Grinding mills can be optimized for maximum ore throughput or maximum grinding energy efficiency. In both cases, precise control of the mill weight is critical. Model predictive control provides an additional tool to improve the control of Semi-Autogenous Grinding mills and is often able to reduce process variability beyond the best performance that can be obtained with proporti...

Stochastic optimization for optimal and model-predictive control

The integrated-controlled-random-search for dynamic systems (ICRS/DS) method is improved to include a moving-grid strategy and is applied to more challenging problems including: (1) the optimal control of a fed-batch bioreactor, a plug-flow reactor exhibiting a singular arc, the van der Pol oscillator; and (2) the model-predictive control (MPC) of the Czochralski (CZ) crystallization process. T...