Optimal Adaptive Control of an Ash Stabilization Batch Mixing Process using Change Detection
(2000) IEEE International Conference on Control Applications, 2000 In Control Applications, 2000. Proceedings of the 2000 IEEE International Conference on Abstract
 We present an optimal adaptive controller that is used to regulate the chemical process of wood ash stabilization (WAS). The model parameters of the timevarying process dynamics are estimated using recursive least squares (RLS). At each batch an autotuning sequence produced with the controller disabled is carried through in order to obtain a good estimate of the process dynamics. After the autotuning sequence is completed, a generalized predictive controller is enabled to control the WAS process. The control objective is to regulate the normalized effective power Pe(t) to the level Pe crit that represents the critical rate of useful work being performed by the threephase asynchronous machine used for the stirrer drive. Hence, Pecrit... (More)
 We present an optimal adaptive controller that is used to regulate the chemical process of wood ash stabilization (WAS). The model parameters of the timevarying process dynamics are estimated using recursive least squares (RLS). At each batch an autotuning sequence produced with the controller disabled is carried through in order to obtain a good estimate of the process dynamics. After the autotuning sequence is completed, a generalized predictive controller is enabled to control the WAS process. The control objective is to regulate the normalized effective power Pe(t) to the level Pe crit that represents the critical rate of useful work being performed by the threephase asynchronous machine used for the stirrer drive. Hence, Pecrit also represents the desired mixture viscosity. If more water is added to the stabilization process after Pecrit has been reached, one will obtain a mixture useless for granular material. To cope with this problem, change detection is used to reach the desired level Pecrit without any predetermined setpoint. Two methods are evaluated; a probing strategy and the geometric moving average test, both adequate for successful implementation. The used control strategies are presented and offline simulations with a model of the physical process evaluate the control performance (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/4358647
 author
 Svantesson, T. and Olsson, Gustaf ^{LU}
 organization
 publishing date
 2000
 type
 Chapter in Book/Report/Conference proceeding
 publication status
 published
 subject
 keywords
 adaptive control batch processing (industrial) closed loop systems least squares approximations mixing predictive control process control recursive estimation tuning
 in
 Control Applications, 2000. Proceedings of the 2000 IEEE International Conference on
 conference name
 IEEE International Conference on Control Applications, 2000
 external identifiers

 Scopus:0034474822
 ISBN
 0780365623
 DOI
 10.1109/CCA.2000.897408
 language
 English
 LU publication?
 yes
 id
 70db203aabb74b0f8ffc21606cebc1b2 (old id 4358647)
 date added to LUP
 20140318 09:15:06
 date last changed
 20161013 04:51:43
@misc{70db203aabb74b0f8ffc21606cebc1b2, abstract = {We present an optimal adaptive controller that is used to regulate the chemical process of wood ash stabilization (WAS). The model parameters of the timevarying process dynamics are estimated using recursive least squares (RLS). At each batch an autotuning sequence produced with the controller disabled is carried through in order to obtain a good estimate of the process dynamics. After the autotuning sequence is completed, a generalized predictive controller is enabled to control the WAS process. The control objective is to regulate the normalized effective power Pe(t) to the level Pe crit that represents the critical rate of useful work being performed by the threephase asynchronous machine used for the stirrer drive. Hence, Pecrit also represents the desired mixture viscosity. If more water is added to the stabilization process after Pecrit has been reached, one will obtain a mixture useless for granular material. To cope with this problem, change detection is used to reach the desired level Pecrit without any predetermined setpoint. Two methods are evaluated; a probing strategy and the geometric moving average test, both adequate for successful implementation. The used control strategies are presented and offline simulations with a model of the physical process evaluate the control performance}, author = {Svantesson, T. and Olsson, Gustaf}, isbn = {0780365623}, keyword = {adaptive control batch processing (industrial) closed loop systems least squares approximations mixing predictive control process control recursive estimation tuning}, language = {eng}, series = {Control Applications, 2000. Proceedings of the 2000 IEEE International Conference on}, title = {Optimal Adaptive Control of an Ash Stabilization Batch Mixing Process using Change Detection}, url = {http://dx.doi.org/10.1109/CCA.2000.897408}, year = {2000}, }