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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 "

"ABSTRAKModel sintetik radial receiver function pada lapisan mantle, kerak, dan sedimen (SCM) dibuat dengan memvariasikan ketebalan sedimen mulai dari 0.4 hingga 1.0 km. Forward modelling dijalankan menggunakan program hrftn pada Computer Program in Sesimology (CPS). Parameter data sintetik yang digunakan adalah Vp, Vs, Vp/Vs, dan densitas yang masing-masing nilainya adalah 1.8 km/s, 0.61 km/s, 2.95 dan 2.33 g/cm³ untuk lapisan sedimen. Untuk lapisan kerak Vp, Vs, Vp/Vs, dan densitasnya masing-masing adalah 6.1 km/s, 3.52 km/s 1.73 dan 2.73 g/cm³. Dengan memvariasikan ketebalan sedimen didapatkan respon sinyal receiver function yang mengandung multiple ditandai dengan adanya waktu tunda yang terjadi pada masing-masing fase. Waktu tunda yang terjadi dipengaruhi oleh ketebalan sedimen dan multiple yang terjadi. Multiple yang ada pada receiver function berhasil dikurangi dengan menerapkan filter prediktif. Filter diaplikasikan pada data sintetik receiver function terlebih dahulu kemudian diaplikasikan pada data real receiver function. Parameter filter yang didapat adalah kekuatan multiple yang berasosiasi dengan koefisien refleksi dan waktu bolak-balik pada setiap event gempa. Filter diterapkan kembali pada data real di stasiun BKNI dan BASI. Filter yang diaplikasikan pada data sintetik dan data real cukup efektif untuk mengurangi multiple pada receiver function.

 

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A synthetic radial receiver function model has been prepared on mantle, crust, and sedimentary layers (SCM) by varying the thickness of the sediment from 0.4 until 1.0 km. Forward modelling models are executed with the hrftn program in the Computer Program in Sesimology (CPS). Synthetic data parameters for sedimentary layer using Vp, Vs, Vp/Vs, and density which values are 1.8 km/s, 0.61 km/s, 2.95 and 2.33 g/cm³ respectively. Crustal layer using the same parameter with sedimentary layer that are values 6.1 km/s, 3.52 km/s, 1.73, and 2.73 g/cm³ respectively. By varying the thickness of the sediment obtained the respons of reverberations receiver function signals characterized by delay on every phase. The delay of receiver function caused by sediment thickness and multiple/reverberation. Multiple on receiver function have sucsessfully reduced by applying a filter predictive. The Predictive filter applied on synthetic data and then on real data of receiver function. Parameters of filter have obtained by the filter are strong of reverberation that is associated with the reflection koeficient and two way time of reverberation obtained of every event. Then filter applied on real data on BASI and BKNI station. The filter predictive have successfully reducing multiple effect of the receiver function on real and synthetic data. 

 

 

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"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."