User talk:AndreC - Revision history

Alchemyst: /* SAIChE Postgraduate Student Symposium Abstract */

2010-07-27T09:27:20Z

‎SAIChE Postgraduate Student Symposium Abstract

Alchemyst: /* SAIChE Postgraduate Student Symposium Abstract */

2010-07-27T09:26:46Z

‎SAIChE Postgraduate Student Symposium Abstract

137.215.6.53: /* SAIChE Postgraduate Student Symposium Abstract */

2010-07-27T09:25:56Z

‎SAIChE Postgraduate Student Symposium Abstract

AndreC: more pruning

2010-07-27T08:47:47Z

more pruning

AndreC: pruning

2010-07-21T17:19:21Z

pruning

Alchemyst: Rewording

2010-07-21T06:35:33Z

Rewording

137.215.116.173 at 16:34, 20 July 2010

2010-07-20T16:34:46Z

137.215.116.173 at 16:30, 20 July 2010

2010-07-20T16:30:03Z

AndreC: SAIChE abstract

2010-07-20T16:13:13Z

SAIChE abstract

New page

== SAIChE Postgraduate Student Symposium Abstract ==

Abstract for Masters project up for discussion/comments.

<center>'''<big>A Systematic Approach to Multivariable Predictive Controller Constraint Handling: Rigorous Geometric Methods</big>'''</center><br/>
<center>André H. Campher and Carl Sandrock<br/>
''Department of Chemical Engineering, University of Pretoria''</center>

The models used in multivariable predictive controllers (MPCs) are, typically, mathematically unconstrained. Certain MPC algorithms allow constraints to be imposed on the inputs or outputs of a system, but do so in a manner that is decoupled from the process model. This ad hoc specification can lead to infeasible and unrealistic constraints being specified for controllers. A method to determine and handle input/output constraints, taking their inherent interdependence into account, would therefore be an attractive addition to commercial MPC packages.

A rigorous and systematic approach to constraint management was developed, using the operability index (Vinson & Georgakis, 2000) as the basis for the design. 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.
Focus was placed on making the method rigorous with regards to the use of the process model and the clear distinction between constraint types. Ample feedback to the user was added to the interface to increase interactivity.

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.

@@ Line 3: / Line 3: @@
 Abstract for Masters project up for discussion/comments.<br/>
-Current word count: 256 [250 max]
+Current word count: 249 [250 max]
 <!--Paragraph with reference (Georgakis) feels bulky - maybe the specific reference should be dropped? -->

← Older revision		Revision as of 09:26, 27 July 2010
Line 21:		Line 21:
	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.		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®. These 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.	+	The outputs of the program are compatible with commercial MPC packages, such as Honeywell’s RMPCT® and AspenTech’s DMCPlus®. These 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.

@@ Line 11: / Line 11: @@
 ''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- 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.
+The models used by model predictive controllers (MPCs) to predict future outcomes are usually unconstrained forms like impulse or step responses and discrete state space models. Certain MPC algorithms allow constraints to be imposed on the inputs or outputs of a system; but they may be infeasible as they are not checked for consistency via the process model. Consistent constraint handling methods -- which account for their interdependence and disambiguate 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 in interpreting constraint interactions. The method supports linear and non-linear (polynomial) steady-state systems, and provides an interface where the following information can be obtained;
+A rigorous and systematic approach to constraint management has been developed, building on the work of Georgakis and others in interpreting constraint interactions. The method supports linear and non-linear (polynomial) steady-state system models, and provides an interface where the following information can be obtained;
 * effects of constraint changes on the corresponding input/output constraints,
 * feasibility checks for constraints,

@@ Line 3: / Line 3: @@
 Abstract for Masters project up for discussion/comments.<br/>
-Current word count: 266 [250 max]
+Current word count: 256 [250 max]
 <!--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 Model Predictive Controller Constraint Handling: Rigorous Geometric Methods</big>'''</center><br/>
 <center>André H. Campher and Carl Sandrock<br/>
 ''Department of Chemical Engineering, University of Pretoria''</center>
@@ Line 13: / Line 13: @@
 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;
+A rigorous and systematic approach to constraint management has been developed, building on the work of Georgakis and others in interpreting constraint interactions. The method supports linear and non-linear (polynomial) steady-state systems, and provides an interface where the following information can be 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.
+It was ensured that the outputs of the program were compatible with commercial MPC packages, such as Honeywell’s RMPCT® and AspenTech’s DMCPlus®. These 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.

@@ Line 3: / Line 3: @@
 Abstract for Masters project up for discussion/comments.<br/>
-Current word count: 283 (20 over)
+Current word count: 266 [250 max]
 <center>'''<big>Systematic Multivariable Predictive Controller Constraint Handling: Rigorous Geometric Methods</big>'''</center><br/>
@@ Line 9: / Line 11: @@
 ''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 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.
+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 interactive interface where the following actions can be taken and information obtained;
+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 and other input/output constraints,
+* effects of constraint changes on the corresponding input/output constraints,
-* feasibility checks for given constraints,
+* feasibility checks for constraints,
-* constraint type information,
+* constraint-type information,
-* specification of constraint-set size (number of constraints to use) and
+* specification of constraint-set size and
-* optimal fitting of output constraints within the desirable output space.
+* 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 the interface to provide an supportive rather than prescriptive environment.
+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.

← Older revision		Revision as of 16:34, 20 July 2010
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		+	__FORCETOC__
	== SAIChE Postgraduate Student Symposium Abstract ==		== SAIChE Postgraduate Student Symposium Abstract ==

@@ Line 1: / Line 1: @@
 == SAIChE Postgraduate Student Symposium Abstract ==
-Abstract for Masters project up for discussion/comments.
+Abstract for Masters project up for discussion/comments.<br/>
+Current word count: 270 (20 over)
 <center>'''<big>A Systematic Approach to Multivariable Predictive Controller Constraint Handling: Rigorous Geometric Methods</big>'''</center><br/>