JOL RNAL OF SCIENCE iTECHNOLOC^ • No.83B-201l DESIGNING AN ARFITICIAL NEURAL NETWORK MODEL PREi).c LIVE CONTROLLER FOR MIMO PROCESSES USING ATMEGA 128 THIET KE BO DIEU KHIEN DU BAO DU'A MO HJNH MANG NO-RON \H.\N TAO CHO HE NHIEU CHl£u B A N G ATMEGA 128 Phan Xuan Minh, Doan Van Due Hanoi University of Science and TechnologyABSTRACT In this paper, one method to design an Artificial Neural Network Model Predictive Controki (ANNMPC) for MIMO processes Is presented The optimization problem Is solved by two methods Nonlinear Optimization (NO) and Nonlinear Prediction 8, Linearization (NPL) The proposed controki Is implemented on a developed kit using Microcontroller Atmega 128 The obtained controller is the: used to control a simulator of a Distillation Column plant with two inputs and two outputs Tht experimental results demonstrate the usefulness of ANNMPC for MIMO processes Keywords: Artihclal Neural Network Model Predictive Control (ANNMPC), Multi-Layer Perceptron (MLP), Multiple Input Multiple Output (MIMO), Nonlinear of Optimization (NO), Nonlinear Prediction and Linearization (NPL), Genetic Algonthm (GA), Distillation Column TOM TAT Bai bio trinh biy vi mdt phwang phip thiit ki bd diiu khiin dw bio dwa md hinh mang naron nhin tao cho cic ddl twgng nhiiu diu vio nhiiu diu Bii toin tdi wu hoi dwgc thwc hien bing tiai phwang phip: til wu phi tuyin (NO), dw bio phi tuyin va tuyin tinh hoi (NPL) Thuat toin diiu ktiik di xuit dwoc di dat tren vi diiu khiin Atmega 128 vi dwgc kiim chirng bing md hinh thip chwng dt hai via hai Cic kit qua thwc nghiem cho thiy ning u'ng dung cua bd diiu khiin ANNMPC cfio ddi twgng nhiiu chiiu >^ Conclusion II MODEL PREDICTIVE CONTROL BASED ON ARTIFICIAL NEURAL NETWORK FOR MIMO PROCESSES 2.1 Structure of the neural model To model the nonlinear dynamic characteristic of each MISO process, a structure of NARX (Nonlinear AuloRegressive willi exogenous input) is used I INTRODUC TION In recent vears the MPC for MIMO Svslems has received much attention to universal approximation as artificial neural network (.ANN) or Fuzzy Logic System The ANN is used as a predictive model of MPC because of the abilitv to approximate the process dvnamic exactiv The intelligent control algorithm is implemented on the Atmega 128, one micro controller chip of AlAlll A real experiment, in vvhich the designed controller is used to control a MIMO real-time simulation system with two inputs and two outputs, is developed The paper includes: •^ Introduction •^ Model Predictive Control for MIMO processes based on Artificial Neural Network -^ Designing a ANNMPC using mierocontroller Atmega 128 -^ Applving ANNMPC to control the real Disiillalion Column plant simulator '7 ' T" • ; > , Figure Structure oflhe SARX network 78 JOLRNAL OF SCIENCE & TECHNOLOGV * No 83B-2011 The relationship between inputs and outputs of the network is described as follows v„,(A:) = (7„,(r„(A:)) v.(v)=::.+5^Iii:(:)Wffl:{!,:)^.(^) + /fi"i:)) (2.1) Where m = 1,2, , iLy lil(fe-T" ') 2.2 MPC algorithms based on the structure of Artificial Neural Network [1,2,5] ui(fc-wr) ^'n,fk - The cost function: lu) Kk) = W'fik) Constraints: ^m(.k) (2.4) -.y(;c)|f, + \lAUik)\\l "njfc-A/r-) 3m(fc - 1) Y„ ,