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"Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors"

"ABSTRAKSaat ini Indonesia masih mengalami defisit BBM sehingga diperlukan pembangunan kilang minyak baru dan optimasi proses pada kilang yang sudah ada. Terdapat unit operasi sekunder berupa VDU vacuum distillation unit untuk mengolah produk residu atmosferik dari CDU crude distillation unit . Dalam rangka menjaga kestabilan operasi diperlukan sistem pengendalian yang tepat dan optimum. Oleh karena itu, dalam penelitian ini akan dilihat apakah pengendali Multi Variabel Model Predictive Control MMPC lebih baik dibandingkan dengan pengendali konvensional prorportional-integral, PI dan pengendali lanjut model predictive control, MPC untuk mengendalikan kombinasi laju alir umpan dan suhu bottom stage kolom distillasi. Pengujian kinerja dilakukan dengan melakukan perubahan set-point 50 pada laju alir umpan dan penurunan suhu sampai dengan 354 oC yang merupakan batas bawah pada simulasi ini. Perbandingan dengan studi sebelumnya diukur menggunakan nilai ISE integral square error -nya. Pada penelitian ini didapatkan ISE untuk laju alir umpan dan suhu bottom stage sebesar 351,78 dan 4,25 secara berurutan. Hasil tersebut mengindikasikan adanya peningkatan ISE pengendalian laju alir sebesar 21,13 . dan peningkatan ISE pengendalian suhu Bottom Stage adalah 26,59 .

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ABSTRACTCurrently, Indonesia is still experiencing a fuel deficit, so it is necessary to build a new refinery and process optimization at an existing refinery. There is a secondary operating unit of VDU vacuum distillation unit to process the atmospheric residue product from CDU crude distillation unit . In order to maintain the stability of the operation required a proper control system and optimum. Therefore, in this research will be seen whether Multi Variable Model Predictive Control MMPC controller is better than conventional prorportional integral, PI and Model Predictive Control MPC controller to control the combination of feed flow rate, bottom stage temperature of the distillation coloumn. The performance test was performed by changing the set point to 50 of its original for the feed flow rate and bottom stage temperature is set to 354 oC which is the minimum allowed temperature in this simulation. Comparison with previous study is measured using ISE integral square error . In this study, ISEs obtained for feed flow rate and bottom stage temperature are 351.78 and 4.25 respectively. These results indicate an increase in ISE flow rate control by 21.13 . and the increase in ISE Bottom Stage temperature control is 26.59 "

"This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations;Ø Nonlinear systems described by first-principles models and nonlinear systems described by black-box models;- Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs;- Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty);- Nonlinear systems, consisting of interconnected nonlinear sub-systems.The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers. "

"In electric power systems that consist of some generators, electric power stability in supplies sidebecomes the most important problems, which must be paid attention. In the interconnection system, ifthere are some troubles in transmission, generator or load will cause another generators feel theexistence of instability condition. For instability condition which not too serious, system can overcomethe fault and will not influence stability of system as a whole. However, for in big scale of fault andhappened in a long duration can be ejected system becoming unstable and will result hampered ofelectrics energy supply to the load For the worst condition could be blackout condition.This article studies about improvement of the stability of the system by using excitation current andthe prime mover of generators, which is coordinated fuzzy logic control in synchronize generator. Byusing annexation from three methods above, the condition of stability of the power system can attain thestability. The transient stability needed control in order that system with good stability can return tonormal condition. Faulted electric power system often caused by failure in controlling the transientstability. It is because in transient stability forms critical condition for electrical power system. By controlling the level of excitation current and mechanical energy from the prime mover ofgenerators which controlled by fuzzy logic when the fault is happened will make acceleration areabecome decreasing and deceleration area become increasing with the result that system can be stablequickly. It visible that from result of simulation obtained if using generator oscillation of fuzzy logiccontrol, transient period becoming shorter and amplitude of oscillation wave is smaller compare by usingwithout fuzzy logic. Likewise, this method is able loo to overcome transient condition at starting period ofa generator."

"Penelitian ini bertujuan untuk merancang sebuah sistem control dari sebuah robot inverted pendulum menggunakan Model Predictive Control. Dalam penelitian akan digunakan sensor sudut dan posisi sebagai data masukkan untuk komputasi nilai keluaran yang optimal yang perlu diberikan kepada servo dan motor. Komputasi akan dilakukan di komputer yang dihubungkan dengan robot menggunakan protokol komunikasi UART. Program pada komputer juga akan menampilkan kondisi robot. Model Dinamika yang digunakan akan disimulasikan terlebih dahulu sebelum digunakan. Robot dapat mengirimkan data dari sensor dan menjalankan keluaran optimal yang sudah dikomputasi.

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This research is aimed to design a control system from inverted pendulum robot using Model Predictive Control. This research will be using angular and position sensor as input for computing the optimal output for the motor and servo. The computation will be done by a computer that is connected with the robot using UART Communication Protocol. The program that is runned by the computer will also display the robot condition. Dynamics model that will be used will be simulated first before real application. The inverted pendulum robot is able to send data from sensor to the computer and run the optimal output that has been computed."

"The book contains 26 scientific contributions by leading experts from Russia, Austria, Italy, Japan and Taiwan. It presents an overview on recent developments in Advanced dynamics and model based control of structures and machines. Main topics are nonlinear control of structures and systems, sensing and actuation, active and passive damping, nano- and micromechanics, vibrations and waves."

"Transportasi umum kereta antar kota menjadi salah satu transportasi yang banyak diminati karena bisa mengurangi kemacetan dan jarak tempuh yang jauh serta bisa mengangkut orang dalam jumlah yang banyak. Pada kenyataannya, bahan bakar yang digunakan adalah diesel yang Smenghasilkan emisi gas karbon. Emisi gas karbon bisa meningkatkan pencemaran udara dan efek rumah kaca. Sebagai solusi, penggunaan kombinasi sumber energi pada sistem kereta hibrid sudah dilakukan dan dikembangkan dalam beberapa dekade ini. Sumber energi listrik dalam bentuk baterai atau sel bahan bakar hidrogen dalam bentuk fuel cell menjadi energi utama dalam penggerak kereta. Sebagai tambahan, untuk memenuhi kebutuhan daya kereta yang disesuaikan juga bisa ditambahkan sumber energi lain seperti ultra/super capacitor ataupun diesel. Oleh karena itu, pada sistem kereta hibrid ini diperlukan EMS (Energy Management System) untuk mengatur keluaran dan penggunaan energi yang dipakai secara optimal. Metode pengendalian yang digunakan adalah MPC (Model Predictive Control) yang memungkinkan strategi perencanaan dan pengoptimalan penggunaan energi dari berbagai sumber daya. Pada metode optimasi MPC terdapat cost function yang akan diminimalisir dan dioptimalkan dengan berbagai constraints.

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Inter-city train public transportation is one of the most popular transportations because it can reduce congestion and long distances and transport large numbers of people. In reality, the fuel used is diesel which produces carbon gas emissions. Carbon gas emissions can increase air pollution and the greenhouse effect. As a solution, the use of a combination of energy sources in hybrid train systems has been carried out and developed in recent decades. Electrical energy sources in the form of batteries or hydrogen in the form of fuel cells become the main energy in driving the train. In addition, other energy sources such as ultra/super capacitors or diesel can also be added to fulfill the power requirements of the train. Therefore, this hybrid train system requires an EMS (Energy Management System) to optimally manage the output and use of energy used. The control method used is MPC (Model Predictive Control) which enables strategic planning and optimization of energy use from various resources. In the MPC optimization method, there is a cost function that will be minimized and optimized with various constraints."

"Kemampuan produksi minyak di Indonesia semakin menurun sejak tahun 1997 hingga sekarang sedangkan kebutuhan produk minyak/ BBM menunjukkan kecenderungan yang semakin meningkat. Maka produk dimetil eter (DME) dapat digunakan sebagai sumber energi alternatif yang lebih ramah lingkungan dan berkelanjutan. Pada pabrik purifikasi DME ini, umpan dengan komposisi DME, metanol dan air akan dipisahkan sehingga diperoleh DME murni dengan konsentrasi 99%. Dalam proses produksinya, unit-unit proses mengalami banyak gangguan yang berdampak pada menurunnya efisiensi dan kestabilan operasi dan juga berpengaruh pada aspek keselamatan.Pada penelitian ini, pengendali Model Predictive Control (MPC) memiliki kinerja yang lebih baik dibanding pengendali PI dalam mengatasi gangguan dengan penurunan integral of absolute error (IAE) sebesar 40,08% hingga 96,26% dari pengendali PI. Parameter penyetelan (tuning) pada pengendali MPC yang berupa sampling time (T), prediction horizon (P), dan control horizon (M) dicari menggunakan metode non-adaptive dan fine tuning. Analisis kelaikan ekonomi pemasangan MPC menunjukkan bahwa payback period adalah sebesar 14,5 tahun dan 13,4 tahun serta net present value (NPV) sebesar -11juta rupiah dan -9,3 juta rupiah pada skenario gangguan umpan 5% dan 8% secara berturut-turut, sehingga penggantian pengendali dari PI menjadi MPC pada pabrik purifikasi DME secara ekonomi tidak menguntungkan.

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Oil and gas production in Indonesia always decreasing since 1997 until now, and yet the need of oil and fuel product show increasing trajectory. Dimethyl ether (DME) can be used as altenative energy source, it is environmentally safe and sustainable. In this DME purification plant, feed stream containing DME, methanol, and water mixture is separated to obtain DME with 99% purity. In its production process, process unit in DME plant must get disturbances that will affect to the decreasing of process efficiency, operation stability and even safety aspect.

In this research, Model Predictive Control (MPC) has better performance than PI controller in order to overcome disturbances with error (IAE) reduction ranging from 40,08% up to 96,26% than PI controller. Tuning parameters in MPC controller, which are sampling time (T), prediction horizon (P) and control horizon (M), are estimated by both non-adaptive and fine tuning method. Economic feasibility analysis on MPC controller implementation shows that the payback period is 14,5 years and 14,3 years, then NPV -11 million rupiah and -9,3 million rupiah in disturbance scheme of 5% and 8% respectively . Hence, it is not economically feasible to change PI controller into MPC controller on dimethyl ether purification plant."

"ABSTRAKMMPC (Multivariable Model Predictive Control) digunakan untuk mengontrol suhu dan tekanan di kilang regasifikasi LNG untuk mengatasi masalah yang saling mempengaruhi variabel dan mengurangi jumlah pengontrol. Ada empat variabel yang dikontrol (variabel terkontrol, CV) dan empat variabel yang dimanipulasi variabel, MV). CV yang dikontrol adalah tekanan di tangki penyimpanan LNG yaitu tekanan keluaran vaporizer, suhu keluaran vaporizer, dan suhu gas ke pipa. MV dimanipulasi, yang masing-masing berpasangan dengan CV tersebut, adalah laju aliran produk tank top, laju aliran gas pipa, laju aliran air laut, dan pemanas tugas. Identifikasi Model empiris FOPDT (First Order Plus Dead-Time) akan dilakukan terhadap keempatnya pasang CV dan MV untuk menggambarkan interaksi antar variabel. FOPDT diperoleh digunakan sebagai pengontrol di MMPC dan menentukan pengaturan kinerja kontrol Parameter MMPC yaitu P (prediction horizon), M (control horizon), T (waktu sampling). Kinerja kontrol diukur dengan menggunakan metode ISE (Integral Square Error). Hasilnya, parameter MMPC (P, M, T) untuk kondisi regasifikasi LNG adalah optimum masing-masing adalah 330, 1, 1. Ukuran ISE dari pengontrol MMPC dalam setpoint pelacakan: 2.12 × 10-4; 23.834; 0,763; 0,085, dengan perkembangan kinerja pengontrol masing-masing 31.262%, 17%, 175%, 757% dibandingkan kinerja MPC.

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ABSTRACTMMPC (Multivariable Model Predictive Control) is used to control temperature and pressure in the LNG regasification plant to overcome the problem of interplaying variables and reducing the number of controllers. There are four controlled variables (controlled variable, CV) and four manipulated variablesvariable, MV). CV that is controlled is the pressure in the LNG storage tank, namely the vaporizer output pressure, the vaporizer output temperature, and the gas temperature to the pipe. MV manipulated, each of which is paired with the CV, is the tank top product flow rate, the pipeline gas flow rate, the seawater flow rate, and the heating duty. Identification of the FOPDT (First Order Plus Dead-Time) empirical model will be carried out on the four CV and MV pairs to describe the interactions between variables. The obtained FOPDT is used as a controller in the MMPC and determines the control performance settings for the MMPC parameters, namely P (prediction horizon), M (control horizon), T (sampling time). Control performance is measured using the ISE (Integral Square Error) method. As a result, the MMPC parameters (P, M, T) for the optimum LNG regasification conditions were 330, 1, 1. ISE size of the MMPC controller in the tracking setpoint: 2.12 × 10-4; 23,834; 0.763; 0.085, with the development of the controller performance respectively 31,262%, 17%, 175%, 757% compared to the performance of MPC."