Difference between revisions of "User talk:AndreC"
(Rewording) |
|||
| Line 3: | Line 3: | ||
Abstract for Masters project up for discussion/comments.<br/> | Abstract for Masters project up for discussion/comments.<br/> | ||
| − | Current word count: | + | Current word count: 283 (20 over) |
| − | <center>'''<big> | + | <center>'''<big>Systematic Multivariable Predictive Controller Constraint Handling: Rigorous Geometric Methods</big>'''</center><br/> |
<center>André H. Campher and Carl Sandrock<br/> | <center>André H. Campher and Carl Sandrock<br/> | ||
''Department of Chemical Engineering, University of Pretoria''</center> | ''Department of Chemical Engineering, University of Pretoria''</center> | ||
| − | The models used | + | The models used by model predictive controllers (MPCs) to predict future outcomes are usually unconstrained forms like impulse response, response models or discrete state space models. Certain MPC algorithms allow constraints to be imposed on the inputs or outputs of a system; but, as they are not coupled with the process model and checked for consistency, infeasible or unrealistic constraints can be specified. Methods for handling constraints in a consistent manner, taking into account their interdependence and disambiguating the language used to specify constraints, would therefore be an attractive addition to any MPC package. |
| − | A rigorous and systematic approach to constraint management | + | A rigorous and systematic approach to constraint management has been developed, building on the work of Georgakis and others (for instance, Vinson & Georgakis, 2000) in interpreting constraint interactions. The method supports linear and non-linear (polynomial) steady-state systems, and provides an interactive interface where the following actions can be taken and information obtained; |
* effects of constraint changes on the corresponding and other input/output constraints, | * effects of constraint changes on the corresponding and other input/output constraints, | ||
* feasibility checks for given constraints, | * feasibility checks for given constraints, | ||
| Line 17: | Line 17: | ||
* specification of constraint-set size (number of constraints to use) and | * specification of constraint-set size (number of constraints to use) and | ||
* optimal fitting of output constraints within the desirable output space. | * optimal fitting of output constraints within the desirable output space. | ||
| − | + | Mathematical rigour and unambiguous language for identifying constraint types were key design criteria. Ample feedback to the user was added to the interface to provide an supportive rather than prescriptive environment. | |
It was ensured that the outputs of the program were compatible with commercial MPC packages, such as Honeywell’s RMPCT® and AspenTech’s DMCPlus®. The aforementioned packages were used in conjunction with the developed software to test functionality and performance of the method. The method was applied to case studies from Anglo Platinum, the Tennessee Eastman sample problem and laboratory scale test rigs. | It was ensured that the outputs of the program were compatible with commercial MPC packages, such as Honeywell’s RMPCT® and AspenTech’s DMCPlus®. The aforementioned packages were used in conjunction with the developed software to test functionality and performance of the method. The method was applied to case studies from Anglo Platinum, the Tennessee Eastman sample problem and laboratory scale test rigs. | ||
Revision as of 08:35, 21 July 2010
SAIChE Postgraduate Student Symposium Abstract
Abstract for Masters project up for discussion/comments.
Current word count: 283 (20 over)
Department of Chemical Engineering, University of Pretoria
The models used by model predictive controllers (MPCs) to predict future outcomes are usually unconstrained forms like impulse response, response models or discrete state space models. Certain MPC algorithms allow constraints to be imposed on the inputs or outputs of a system; but, as they are not coupled with the process model and checked for consistency, infeasible or unrealistic constraints can be specified. Methods for handling constraints in a consistent manner, taking into account their interdependence and disambiguating the language used to specify constraints, would therefore be an attractive addition to any MPC package.
A rigorous and systematic approach to constraint management has been developed, building on the work of Georgakis and others (for instance, Vinson & Georgakis, 2000) in interpreting constraint interactions. The method supports linear and non-linear (polynomial) steady-state systems, and provides an interactive interface where the following actions can be taken and information obtained;
- effects of constraint changes on the corresponding and other input/output constraints,
- feasibility checks for given constraints,
- constraint type information,
- specification of constraint-set size (number of constraints to use) and
- optimal fitting of output constraints within the desirable output space.
Mathematical rigour and unambiguous language for identifying constraint types were key design criteria. Ample feedback to the user was added to the interface to provide an supportive rather than prescriptive environment.
It was ensured that the outputs of the program were compatible with commercial MPC packages, such as Honeywell’s RMPCT® and AspenTech’s DMCPlus®. The aforementioned packages were used in conjunction with the developed software to test functionality and performance of the method. The method was applied to case studies from Anglo Platinum, the Tennessee Eastman sample problem and laboratory scale test rigs.
