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"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."

"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. "

"Model sistem tata udara presisi dimodelkan sebagai sebuah sistem multivariable dengan dua output yaitu temperature dan kelembaban dan dua input yaitu kecepatan putaran motor dan bukaan valve. Pada model ini ada masalah coupling diantara input dan outputnya. Model Predictive Control (MPC) adalah salah satu cara untuk mengatasi masalah coupling dalam sistem multivariable. Pengendali MPC dirancang tanpa constraints untuk menentukan agoritma yang handal.Dari hasil simulasi nampak bahwa parameter-parameter pengendali yang terbaik adalah horizon Hp=10, Hu=4, matrik pembobotan R=0.1, dan Q=3. Dengan parameter ini respon keluarannya mengikuti sinyal set point.

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Precision Air Conditioning model is defined as a multivariable system with two outputs Temperature and humidity and two inputs, the speed of motor compressor and valve opening. There will be a coupling problem between inputs and outputs. Model Predictive control (MPC) is a way to counter a coupling problems in multivariable system. MPC controller is designed without constraints addition to determine the reliable algorithm.

From the simulation result, it can be seen that the best parameters controller are horizon Hp=10, Hu=4, weighting matrix R=0.1 and Q=3. In this parameter, the output response equal to the trajectory or set point signal."

"Untuk menangani gangguan pada proses operasi nonlinear diperlukan suatu bentuk pengendalian. Representative Model Predictive Control (RMPC) adalah salah satu cara untuk memperoleh sekumpulan MPC lokal yang dapat merepresentasikan keseluruhan rentang operasi. MPC lokal ini nantinya digunakanpada Multiple Model Predictive Control (MMPC) untuk mensimulasikanproses operasi nonlinear multi variabel.Skripsi ini membahas penggunaan RMPC untuk memilih beberapa MPC lokal yang kemudian digunakan sebagai model pada MMPC untuk menangani gangguan. Penelitian ini menggunakan model kolom distilasi biner ?Kolom A? yang disimulasikan dengan perangkat lunak MATLAB. Variabel yang dimanipulasi adalah laju refluks dan laju boil up sedangkan variabel yang dikontrol adalah komposisi produk distilat dan komposisi produk bawah. Hasil IAE MMPC dibandingkan dengan IAE kontroler PI konvensional. Untuk gangguan single step; MPC terbaik dengan IAE 0,2564, lebih baik dari IAE kontroler PI 0,7494.Sedangkan untuk gangguan multi step; MMPC terbaik dengan IAE 0,7730, lebih baik dari IAE kontroler PI 0,9808.

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In order to handle disturbances in the nonlinear operation some form of control is required. Representative Model Predictive Control (RMPC) is one way to obtain a set of local MPC which able to represent the entire operating range. The local MPC is later used in the Multiple Model Predictive Control (MMPC) to simulate the operation of nonlinear multi-variable process. This thesis discusses the use of RMPC to select some local MPC which is then used as a model for dealing with disturbances in the MMPC. This study uses a model of a binary distillation column "Column A" which is simulated with MATLAB software. The manipulated variable is the rate of reflux and boil-up rate, while the controlled variable is the product composition of the distillate and bottom product composition. MMPC IAE results compared with conventional PI controller IAE. For single step disturbance; the best MPC with IAE 0.2564, is better than PI controller IAE 0.7494. As for the multi-step disturbance; the best MMPC with IAE 0.7730, is better than PI controller IAE 0.9808."

"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"

"Model Predictive Control (MPC) merupakan salah satu metode pengendali prediktif berbasis model yang populer digunakan pada dunia industri. Beberapa keuntungan yang ditawarkan oleh pengendali ini diantaranya adalah kemampuannya dalam menangani sistem multivariabel dengan cukup mudah dan juga kemampuannya untuk memberikan constraints atau batasan tertentu baik pada sinyal pengendali maupun pada keluaran sistem. Sistem Heat Exchanger yang akan digunakan pada tesis ini juga merupakan sistem multivariabel berorde tinggi yang mempunyai dua masukan dan dua keluaran. Model sistem yang dipakai berupa model linear diskrit yang didapat dari linearisasi model linearnya. Hasil pengendalian menggunakan MPC constraints akan dibandingkan dengan MPC unconstraints.

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Model Predictive Control is one of the predictive control methods that popular for being used in industry. Some advantages offered by this controller are its ability to easily handle multivariable system easier and also its ability to give constraints or certain limitation of controller signal/ on output system.

Heat exchanger system which will be controlled here is also high-order multivariable system with two inputs and two outputs. The system model that use is discrete linear model which is get from linearization of linear model. The result of controller using MPC constraints will be compare with MPC unconstraints."

"Penggunaan komponen pasif pada sistem suspensi kendaraan mempunyai beberapa kelemahan, yaitu sistem tidak dapat menyesuaikan dengan kondisi permukaan jalan yang mengurangi kenyamanan serta keamanan dalam berkendara. Untuk mengatasi masalah tersebut dapat dilakukan dengan menambahkan komponen aktif pada sistem suspensi pasif, yang kemudian lebih dikenal dengan sistem suspensi semi-aktif. Sumber tenaga eksternal tidak diperlukan pada suspensi semi-aktif, sehingga hanya perlu mengubah damping koefisien pada damper. Dengan mengendalikan output berupa suspension deflection dari gangguan eksternal berupa kontur jalan pada model kendaraan full car diperlukan pengendali yang prediktif. Salah satu pengendali prediktif yang umum digunakan dan sudah teruji adalah Model Predictive Control (MPC). MPC digunakan untuk mengendalikan sistem suspensi semi-aktif hasil dari identifikasi sistem dengan metode identifikasi least square bertingkat. Pada laporan skripsi ini diajukan metode simulasi untuk hasil kinerja dari sistem yang akan diuji dengan menggunakan perangkat lunak MATLAB. Sedangkan untuk pengambilan data dan melihat hasil kinerja simulasi pada model kendaraan dengan menggunakan simulator Carsim.

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The use of passive components in vehicle suspension systems has several disadvantages, one of them is the system cannot adjust to road surface conditions that reduce comfort and safety in driving. To overcome this problem can be done by adding active components to the passive suspension system, which is then better known as a semi-active suspension system. External power sources are not required for semi-active suspensions, so only need to change the damping coefficient on the damper. By controlling the output in the form of suspension deflection from external disturbances in the form of road contours on a full car vehicle model, a predictive controller is needed. One predictive controller that is commonly used and tested is the Model Predictive Control (MPC). MPC is used to control the semi-active suspension system as a result of identifying the system with the multistage least square identification method. In this thesis report, a simulation method is proposed for the performance results of the system to be tested using MATLAB software. Meanwhile, to data collecting and see the performance results on vehicle models using the Carsim simulator."

"Pada sistem kendali konvensional, batasan-batasan seperti amplitudo dan slew rate sinyal kendali tidak diperhitungkan pada proses pengendalian. Hal ini tentu dapat menyebabkan hasil kendali menjadi kurang baik, terutama jika terjadi pemotongan paksa terhadap sinyal kendali sebelum masuk ke plant. Untuk mengatasi hal tersebut dirancanglah suatu pengendali Model Predictive Control (MPC). Dengan MPC, keluaran proses yang akan datang dapat diprediksi dan batasan-batasan yang ada tidak diabaikan sehingga keluaran sistem menjadi bagus. Selain keluaran sistem menjadi bagus, adanya batasan juga dapat membuat kinerja alat menjadi optimal. Skripsi ini bertujuan untuk merancang jenis pengendali Model PredictiveControl (MPC) yang akan diterapkan pada sebuah sistem nyata Level/Flow and Temperature Process Rig 38-003 dengan metode Quadratic Programming. Dalam merancang pengendali MPC untuk Level/Flow and Temperature Process Rig 38-003 ini, penulis menggunakan model yang berbentuk ruang keadaan yang didapat dengan menggunakan metode Kuadrat Terkecil berdasarkan pada data masukan dan data variabel keadaan alat. Masukan sistem adalah tegangan untuk mengatur kondisi servo valve dan keluran yang akan dikendalikan adalah temperatur air hasil keluaran Heat Exchanger sebelum masuk ke sistem Radiator Cooler. Dari uji eksperimen terbukti bahwa metode pengendali MPC dengan constraints memberikan hasil yang lebih baik dibandingkan dengan metode pengendali Ruang Keadaan. Hal tersebut dapat dilihat dari tanggapan sistem hasil pengendalian MPC dengan constraints yang lebih halus dibandingkan dengan tanggapan sistem hasil pengendalian dengan metode pengendali Ruang Keadaan. Perubahan sinyal kendali pengendali MPC dengan constraints juga jauh lebih halus dibandingkan dengan perubahan sinyal kendali pengendali Ruang Keadaan. Kondisi ini akan meningkatkan ketahanan fisik sistem selama uji eksperimen.

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In conventional control system, some constraints such as amplitude and control signal?s slew rate are not included in the controlling process. So, the result of the control process is not good enough especially if the control signal is forcibly cut before entering the plant. In order to overcome this problem, a Model Predictive Controller is designed. In this MPC control scheme, the few next steps of process output are going to be predicted and some constraints will be ignored so the system output will become precise. In other hand, the occurrence of constraints will improve system?s performance into an optimum condition.

The final purpose of this thesis is to design a Model Predictive Controller (MPC) using Quadratic Programming method which will be applied on a real time system of Level/Flow and Temperature Process Rig 38-003. In designing MPC controller for Level/Flow and Temperature Process Rig 38-003, the writer uses system?s model on state space form which is obtained by using Least Square method in the basis of input and state variables data of the plant. Input for the plant is voltage which will be used to control the position of servo valve whereas the controlled output is water temperature on the pipe that connects Heat Exchanger's output line and Radiator Cooler's input line.

Experiments conducted prove that MPC with constraints controlling scheme will give a better results than State Controller controlling scheme. Generally, it can be seen that system response to MPC controller is much smoother than system response to State Controller. MPC controller also has smoother control signal variance compared to State Controller control signal variance. This condition will actually raise the system's physical reliability during the experiment."

"Turbo expander TE dan Model Predictive Control MPC diusulkan untuk digunakan pada unit depropanizer untuk meningkatkan recovery propana dan memperbaiki kinerja pengendalian di unit tersebut. Model yang digunakan dalam MPC adalah model first-order plus dead time FOPDT, yang diuji kinerja pengendaliannya menggunakan pengujian perubahan set point SP dan gangguan, dengan ukuran kinerjanya menggunakan integral of absolute error IAE. Hasilnya menunjukkan bahwa penggunaan TE pada depropanizer mampu meningkatkan recovery propana sebesar 8,44 dari 82,11 menjadi 90,55. Sedangkan untuk struktur pengendalian, digunakan pengendalian tekanan pada TE menggunakan pengendali proportional-integral, PI, pengendalian komposisi propana pada aliran distilat menggunakan MPC dan pengendalian tekanan kolom depropanizer menggunakan MPC. Setelah melakukan pengujian perubahan SP didapatkan bahwa kinerja pengendali MPC pada pengendali komposisi dan pengendali tekanan depropanizer dapat memperbaiki kinerja pengendali PI sebesar 1,62 dan 93,40. Sedangkan pada pengujian terjadinya gangguan didapatkan bahwa kinerja pengenali MPC pada pengendali komposisi dan pengendali tekanan depropanizer dapat memperbaiki kinerja pengendali PI sebesar 60,54 dan 6-,21 sehingga pengendali MPC lebih baik dibandingkan pengendali PI untuk digunakan pada pengendali komposisi dan pengendali tekanan pada depropanizer yang menggunakan Turbo Expander.

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Turbo expander TE and Model Predictive Control MPC is suggested for depropanizer unit to increase propane recovery and improve control performance of the unit. The model used in the MPC is first order plus dead time FOPDT, which tested the performance of the control using set point and disturbance change test with measurement of the performance using integral of absolute error IAE. As a result, use of TE in the depropanizer able to increase recovery of propane of 8,44 from 82.11 to 90.55. As for the control structure, pressure control is use on the TE using proportional integral control, composition control in the distillate flow using MPC, and pressure control in depropanizer column using MPC.

After doing SP changed test, the result showed performance of MPC controller at composition control and pressure control in depropanizer can improve performance compared by PI controller of 1.62 and 93.40. and then for disturbance rejection test, the result showed the MPC controller perfromance can improve PI controller performance at composition control and pressure control in depropanizer is able to improve PI controller performance by 60.54 and 60.21. So that, MPC controller is better than PI controller if it use at composition controller and pressure controller in depropanizer unit with Turbo Expander."

"Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today.The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance.The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading."