Sparse-Grid-Based Adaptive Model Predictive Control of HL60 Cellular Differentiation

2012 ◽  
Vol 59 (2) ◽  
pp. 456-463 ◽  
Author(s):  
S. L. Noble ◽  
L. E. Wendel ◽  
M. M. Donahue ◽  
G. T. Buzzard ◽  
A. E. Rundell
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Cellular Differentiation ◽  
Sparse Grid ◽  
Adaptive Model ◽  
Adaptive Model Predictive Control ◽  
Grid Based

scholarly journals Data–Driven Indirect Adaptive Model Predictive Control

2012 ◽  
Author(s):  
Norhaliza Wahab ◽  
Mohamed Reza Katebi ◽  
Mohd Fua’ad Rahmat ◽  
Salinda Bunyamin
Keyword(s):  
Model Predictive Control ◽  
Activated Sludge ◽  
State Space ◽  
Predictive Control ◽  
Sliding Window ◽  
Subspace Identification ◽  
Activated Sludge Process ◽  
Adaptive Model ◽  
Time Step ◽  
Adaptive Model Predictive Control

Kertas kerja ini membincangkan tentang reka bentuk Pengawal Ramalan Model Suai menggunakan kaedah Pengenalpastian Model Keadaan Ruang Sub–ruang bagi proses enapcemar teraktif. Penggunaan teknik Pengenalpastian Model Keadaan Ruang Sub–ruang di dalam kaedah kawalan tingkat gelangsar suai dibincangkan di mana pengenalpastian sub–ruang dalam talian menggunakan algoritma N4SID di perkenalkan bersama dengan rekabentuk Pengawal ramalan model. Pembangunan N4SID dalam talian di dalam kertas kerja ini menggunakan pengemaskini QR di mana gabungan di antara teknik kemaskini dan kemasbawah membolehkan pengadaptasi tingkap gelangsar. Di sini, untuk setiap langkah masa, bagi setiap data baru akan dimasukkan ke faktor R manakala data yang lama dibuang. Begitu juga, strategi bagi uraian nilai tunggal diperkenalkan ke dalam Pengawal Ramalan Model Suai tak langsung untuk masukan tambahan kawalan bagi sistem terkekang tak lelurus. Beberapa kajian simulasi bagi parameter kawalan berlainan di dalam pengawal/pengenalpastian algoritma dilaksanakan. Bagi reka bentuk Pengawal Ramalan Model Suai tak langsung, pengiraan masa yang terlibat dengan menggunakan pendekatan uraian nilai tunggal kurang berbanding dengan kaedah perancangan kuadratik dan keputusan yang memberangsangkan ini adalah sumbangan utama di dalam kertas kerja ini. Kata kunci: Pengawal suai; proses enapcemar teraktif; pengawal ramalan model; pengenalpastian sub–ruang This paper explores the design of Adaptive Model Predictive Control (AMPC) using Subspace State–space Model Identification (SMI) techniques for an activated sludge process. The implementation of SMI techniques in the adaptive sliding window control methods are discussed where the online subspace identification using Numerical State–space Subspace System Identification (N4SID) algorithm is proposed along with Model Predictive Control (MPC) design method. The online N4SID algorithm developed in this study makes use of the QR–updating where the combination of update and down date techniques enables sliding window adaptation. Here, at each time step, for the new experimental data added into R factor, the oldest data are removed. Also, the Singular Value Decomposition (SVD–based) strategy is proposed into Indirect AMPC (IAMPC) for the control increment input constrained nonlinear system. Several simulation studies for different control parameters in control/identification algorithm are performed. For the IAMPC control design, the computational times involved using an SVD approach shows less burdensome compared to Quadratic Programming (QP) method and such an interesting result is considered as one of the main contribution in this paper. Key words: Adaptive control; activated sludge process; model predictive control; subspace identification

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