Difference between revisions of "User talk:AndreC"
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Abstract for Masters project up for discussion/comments.<br/> | Abstract for Masters project up for discussion/comments.<br/> | ||
| − | Current word count: | + | Current word count: 266 [250 max] |
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| + | <!--Paragraph with reference (Georgakis) feels bulky - maybe the specific reference should be dropped? --> | ||
<center>'''<big>Systematic Multivariable Predictive Controller Constraint Handling: Rigorous Geometric Methods</big>'''</center><br/> | <center>'''<big>Systematic Multivariable Predictive Controller Constraint Handling: Rigorous Geometric Methods</big>'''</center><br/> | ||
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''Department of Chemical Engineering, University of Pretoria''</center> | ''Department of Chemical Engineering, University of Pretoria''</center> | ||
| − | The models used by model predictive controllers (MPCs) to predict future outcomes are usually unconstrained forms like impulse response, response | + | The models used by model predictive controllers (MPCs) to predict future outcomes are usually unconstrained forms like impulse response-, response- 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 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 | + | 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 interface where the following actions can be taken and information obtained; |
| − | * effects of constraint changes on the corresponding | + | * effects of constraint changes on the corresponding input/output constraints, |
| − | * feasibility checks for | + | * feasibility checks for constraints, |
| − | * constraint type information, | + | * constraint-type information, |
| − | * specification of constraint-set size | + | * specification of constraint-set size and |
| − | * optimal fitting of | + | * optimal fitting of constraints within the desirable input/output space. |
| − | Mathematical rigour and unambiguous language for identifying constraint types were key design criteria. Ample feedback to the user was added | + | Mathematical rigour and unambiguous language for identifying constraint types were key design criteria. Ample feedback to the user was added to provide a 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 19:19, 21 July 2010
SAIChE Postgraduate Student Symposium Abstract
Abstract for Masters project up for discussion/comments.
Current word count: 266 [250 max]
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- 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 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 interface where the following actions can be taken and information obtained;
- effects of constraint changes on the corresponding input/output constraints,
- feasibility checks for constraints,
- constraint-type information,
- specification of constraint-set size and
- optimal fitting of constraints within the desirable input/output space.
Mathematical rigour and unambiguous language for identifying constraint types were key design criteria. Ample feedback to the user was added to provide a 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.
