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Abstrak :
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

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ABSTRAK
Saat 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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ABSTRACT
Currently, 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

Abstrak :
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.

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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. ......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.

Abstrak :
[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., 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.]

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In electric power systems that consist of some generators, electric power stability in supplies side becomes the most important problems, which must be paid attention. In the interconnection system, if there are some troubles in transmission, generator or load will cause another generators feel the existence of instability condition. For instability condition which not too serious, system can overcome the fault and will not influence stability of system as a whole. However, for in big scale of fault and happened in a long duration can be ejected system becoming unstable and will result hampered of electrics 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 and the prime mover of generators, which is coordinated fuzzy logic control in synchronize generator. By using annexation from three methods above, the condition of stability of the power system can attain the stability. The transient stability needed control in order that system with good stability can return to normal condition. Faulted electric power system often caused by failure in controlling the transient stability. 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 of generators which controlled by fuzzy logic when the fault is happened will make acceleration area become decreasing and deceleration area become increasing with the result that system can be stable quickly. It visible that from result of simulation obtained if using generator oscillation of fuzzy logic control, transient period becoming shorter and amplitude of oscillation wave is smaller compare by using without fuzzy logic. Likewise, this method is able loo to overcome transient condition at starting period of a generator.

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ABSTRAK
Dalam banyak kendaraan modern, faktor keamanan menjadi pertimbangan penting dalam mendesain kendaraan. Sebagai salah satu bagian dari Advanced Driver Assistant Systems (ADAS) yang diperuntukkan untuk meningkatkan keamanan dalam berkendara, Adaptive Cruise Control (ACC) diperkenalkan untuk mengurangi kemungkinan terjadinya kecelakaan lalu lintas. Sistem pada ACC dapat membantu pengendara dalam menjaga jarak aman dengan kendaraan yang berada di depannya dengan mengendalikan besaran pada katup gas serta tekanan pada rem. Selain untuk meningkatkan faktor keamanan, sistem ACC harus mampu memberikan respon yang halus agar pengendara tetap merasa nyaman. Pada penelitian ini, sistem ACC akan didesain dengan memanfaatkan metode switching yang memiliki respon yang halus dengan memanfaatkan kecepatan relatif, jarak antar kendaraan, dan percepataan kendaraan untuk menentukan kondisi follow mode ketika terdapat kendaraan di depannya, dan kondisi cruise mode ketika tidak terdapat halangan. Kemudian dalam mengendalikan kecepatan kendaraan, akan memanfaatkan pengendali Neural Network Predictive Control (NNPC) yang mengatur besaran katup gas dan tekanan rem yang diberikan. Metode NNPC akan memanfaatkan model Artificial Neural Network (ANN) dalam melakukan identifikasi model longitudinal kendaraan yang sangat tidak linier, dan menggabungkan dengan metode Model Predictive Control (MPC) untuk melakukan prediksi keadaan dari kendaraan yang dikendalikan. Hasil dari penelitian memperlihatkan bahwa pengendali NNPC serta algoritma switching yang digunakan mampu menjaga jarak dengan kendaraan yang ada di depannya, serta memiliki respon yang cukup halus.

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ABSTRACT
In many modern vehicles, safety is an important consideration in designing a vehicle. As one part of the Advanced Driver Assistant Systems (ADAS) which is intended to improve safety in driving, Adaptive Cruise Control (ACC) is introduced to reduce the possibility of traffic accidents. The ACC system can help the driver maintain a safe distance from the vehicle in front of him by controlling the throttle and the pressure on the brakes. In addition to increasing the safety factor, the ACC system must be able to provide a smooth response so that the driver feels comfortable. In this study, the ACC system will be designed by using a switching method that has a smooth response by utilizing the relative speed, distance between vehicles, and vehicle acceleration to determine the condition of follow mode when there is a vehicle in front of it, and the cruise mode condition when there are no obstacles. Then in controlling vehicle speed, Neural Network Predictive Control (NNPC) controllers will control the amount of throttle and brake pressure applied. The NNPC method will utilize the Artificial Neural Network (ANN) model to identify longitudinal models of vehicles that are highly non-linear, and combine them with the Model Predictive Control (MPC) method to predict the state of the controlled vehicle. The results of the study show that the NNPC controller and switching algorithm used are able to maintain a distance from the vehicle in front of it, and have a fairly smooth response.

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Hidrogen merupakan salah satu gas yang memiliki banyak kegunaan. Salah satunya pada industri kimia. Pada pabrik biohidrogen, unit kompresor merupakan salah satu unit yang penting dalam pabrik biohidrogen dari biomassa. Kompresor berfungsi untuk mencapai tekanan tinggi pada kondisi operasi selanjutnya. Multivariable model predictive control (MMPC) digunakan untuk mengendalikan proses pada pabrik. Untuk mendapatkan pengendalian yang optimal, perlu dilakukan penyetelan. Penyetelan akan dilakukan pada Matlab-Simulink yang diintegrasikan dengan Aspen Plus Dynamics. Sistem pengendalian akan dibuat pada Simulink dan simulasi proses akan dilakukan pada Aspen Plus Dynamic. Penyetelan ini dilakukan dungeon metode Genetic Algorithm dungeon metode pencarian seleksi turnamen. Setelah itu, hasil penyetelan akan dijalankan juga dengan unisim design agar kinerja pengendalian dapat dibandingkan dengan penelitian sebelumnya. Model first order plus dead time (FOPDT) digunakan sebagai model prediksi MMPC. Pada penelitian ini, model FOPDT yang digunakan di MMPC pada Matlab harus dihasilkan dengan cara satuan tekanan keluaran kompresor terlebih dahulu diubah menjadi satuan persentase karena MMPC pada Matlab akan menginterpretasikan variabel-variabel perhitungan dalam satuan persen. Parameter time sampling (T), prediction horizon (P), dan control horizon (M) terbaik yang diperoleh dari metode penyetelan seleksi turnamen pada simulasi dengan unisim untuk perubahan set-point (SP) yaitu 1 detik, 18, dan 3. Untuk uji gangguan parameter T, P, dan M yang diperoleh dengan penyetelan fine tuning terbaik yaitu 1 detik, 341, dan 121. Pada simulasi Matlab-Simulink-Aspen Plus Dynamics, parameter T, P, dan M yang terbaik yaitu 0,05 detik, 18, dan 2 untuk perubahan SP dan 0,05 detik, 7, dan 1 untuk perubahan gangguan. ......Hydrogen is one of the gases that has many uses, including in the chemical industry. In a biohydrogen plant, the compressor unit is one of the important units in the biomass-based biohydrogen plant. The compressor unit works to achieve high pressure for further operational conditions. Multivariable Model Predictive Control (MMPC) is used to control the processes in the plant. To obtain optimal control performance, tuning process is necessary. The tuning process will be conducted in Matlab-Simulink integrated with Aspen Plus Dynamics. The control system will be designed in Simulink, and the process simulation will be executed in Aspen Plus Dynamics. The tuning was done using the Genetic Algorithm with tournament selection search method. Subsequently, the tuning results will also be implemented in Unisim Design to compare the control performance with previous research. The First Order Plus Dead Time (FOPDT) model is applied as the prediction model for MMPC. In this study, the FOPDT model used in MMPC in Matlab must be generated by converting the compressor output pressure unit into a percentage unit due to the MMPC in Matlab will interpret the calculation variables in percent units. For the set-point change, the best time sampling (T), prediction horizon (P), and control horizon (M) parameters that were obtained from the tournament selection tuning method in the simulation with Unisim design are 1 second, 18, and 3. For disturbance testinwere obtainedest parameters are 1 second, 341, and 121 that obtained by fine-tuning method. In the Matlab-Simulink-Aspen Plus Dynamics simulation, the best parameters T, P, and M for set-point changes are 0.05 seconds, 18, and 2, and for disturbance changes are 0.05 seconds, 7, and 1.

Abstrak :
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.