Solving Dynamic Optimisation Problems with Known Changeable Boundaries

2016 ◽  
pp. 29-40
Author(s):  
AbdelMonaem F. M. AbdAllah ◽  
Daryl L. Essam ◽  
Ruhul A. Sarker
Keyword(s):  
Dynamic Optimisation

scholarly journals Pantograph catenary dynamic optimisation based on advanced multibody and finite element co-simulation tools

2014 ◽  
Vol 52 (sup1) ◽  
pp. 338-354 ◽  
Author(s):  
Jean-Pierre Massat ◽  
Christophe Laurent ◽  
Jean-Philippe Bianchi ◽  
Etienne Balmès
Keyword(s):  
Finite Element ◽  
Dynamic Optimisation ◽  
Simulation Tools

scholarly journals On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems

2018 ◽  
pp. 72-87
Author(s):  
Edgar Galván-López ◽  
Lucia Vázquez-Mendoza ◽  
Marc Schoenauer ◽  
Leonardo Trujillo
Keyword(s):  
Dynamic Optimisation

Performance Analysis of Three Controllers for the Polymerisation of Styrene in a Batch Reactor

2007 ◽  
Vol 2 (1) ◽  
Author(s):  
Emmanuel E Ekpo ◽  
Iqbal M Mujtaba
Keyword(s):  
Performance Analysis ◽  
Batch Reactor ◽  
Monomer Conversion ◽  
Average Chain Length ◽  
Generic Model ◽  
Batch Reactors ◽  
Dynamic Optimisation ◽  
Model Control ◽  
Simple Kinetic Model ◽  
Generic Model Control

The performance analysis of three advanced non linear controllers is the main focus of this paper. All three controllers are applied for the control of a batch polymerisation reactor which is defined by a very simple kinetic model for the polymerisation of styrene. This simple set of equations describing the polymerisation process is first solved using the sequential strategy i.e. Control Vector Parameterisation (CVP) technique within gPROMS to find optimal initial initiator concentrations and the reactor temperature trajectory necessary to yield desired polymer molecular properties (defined here as fixed values of monomer conversion and number average chain length) in minimum time. The sequential solution strategy has had limited application in solving optimisation problems for polymerisation in batch reactors, most researchers instead employing the Pontryagin's Maximum Principle (PMP) to solve optimal control problems involving these systems.The temperature trajectory obtained from the dynamic optimisation is used as the setpoint to be tracked by the three controllers: Dual Mode control with PID, which is representative of industrial practice, Generic Model Control (GMC) with Neural Networks as online heat release estimator, and Direct Inverse Control (DIC). Published work on the last two controllers as applied to the control of a batch polymerisation reactor is absent from the literature.When the performances of the different controllers are evaluated, it is seen that the GMC-NN controller performs better than the other two for the system under consideration.

Which data should be tracked in forward-dynamic optimisation to best predict muscle forces in a pathological co-contraction case?

2018 ◽  
Vol 68 ◽  
pp. 99-106 ◽  
Author(s):  
Colombe Bélaise ◽  
Benjamin Michaud ◽  
Fabien Dal Maso ◽  
Katja Mombaur ◽  
Mickaël Begon
Keyword(s):  
Muscle Forces ◽  
Dynamic Optimisation

Analysing Human Walking Using Dynamic Optimisation

2015 ◽  
pp. 1-34
Author(s):  
Meiyi Tan ◽  
Leslie S. Jennings ◽  
Song Wang
Keyword(s):  
Human Walking ◽  
Dynamic Optimisation

Dynamic optimisation for highly agile supply chains in e-procurement context

2018 ◽  
Vol 56 (17) ◽  
pp. 5904-5929 ◽  
Author(s):  
Akram Chibani ◽  
Xavier Delorme ◽  
Alexandre Dolgui ◽  
Henri Pierreval
Keyword(s):  
Supply Chains ◽  
Dynamic Optimisation
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