SPM: Fast and Robust Model Predictive Control

Fast and Robust Model Predictive Control

Funding Source

Details

Source:	University of Newcastle
Duration:	2009
Abstract:	Automatic computer control is fundamental to sustaining a wide range of manufacturing, mineral processing, chemical processing, and other industries vital to the Australian economy. Furthermore, the efficiency, profitability, and environmental impact of these operations is directly linked to the quality of this computer control. In many situations, even a few percent improvement in automatic control delivers dividends measured in many millions of dollars. This project will develop design tools allowing for more sophisticated, high performance control to be more widely employed. This will deliver the potential for economic and environmental benefits and energy savings to be achieved across a range of industries.

Projects Funded

Model Predictive Control

This project is concerned with development of algorithms and hardware for high-speed model predictive control (MPC) solutions. Both linear and nonlinear MPC systems are considered.

Team Members: Adam Mills, Geoff Knagge, Dr. Adrian Wills

Sub-Projects

QPC - Quadratic Programming in C

This project offers a collection of software routines for solving quadratic programming problems that can be written in this form


 x* = arg min   0.5x'Hx + f'x   convex cost

          s.t.  Ax  = b,        linear equality constraints,

                Lx <= k,        general linear inequality constraints,

                l  <= x <= u,   bound constraints.

The routines are written in C and callable from Matlab using the standard syntax. State-of-the-art solvers are available.

Team Members: Dr. Adrian Wills, Prof. Brett Ninness